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Accelerate the modernization of Mainframe and VMware workloads with AWS Transform

Post Syndicated from Matheus Guimaraes original https://aws.amazon.com/blogs/aws/accelerate-the-modernization-of-mainframe-and-vmware-workloads-with-aws-transform/

Generative AI has brought many new possibilities to organizations. It has equipped them with new abilities to retire technical debt, modernize legacy systems, and build agile infrastructure to help unlock the value that is trapped in their internal data. However, many enterprises still rely heavily on legacy IT infrastructure, particularly mainframes and VMware-based systems. These platforms have been the backbone of critical operations for decades, but they hinder organizations’ ability to innovate, scale effectively, and reduce technical debt in an era where cloud-first strategies dominate. The need to modernize these workloads is clear, but the journey has traditionally been complex and risky.

The complexity spans multiple dimensions. Financially, organizations face mounting licensing costs and expensive migration projects. Technically, they must untangle legacy dependencies while meeting compliance requirements. Organizationally, they must manage the transition of teams who’ve built careers around legacy systems and navigate undocumented institutional knowledge.

AWS Transform directly addresses these challenges with purpose-built agentic AI that accelerates and de-risks your legacy modernization. It automates the assessment, planning, and transformation of both mainframe and VMware workloads into cloud based architectures, streamlining the entire process. Through intelligent insights, automated code transformation, and human-in-the-loop workflows, organizations can now tackle even the most challenging modernization projects with greater confidence and efficiency.

Mainframe workload migration
AWS Transform for mainframe is the first agentic AI service for modernizing mainframe workloads at scale. The specialized mainframe agent accelerates mainframe modernization by automating complex, resource-intensive tasks across every phase of modernization — from initial assessment to final deployment. It streamlines the migration of legacy applications built on IBM z/OS Db2, including COBOL, CICS, DB2, and VSAM, to modern cloud environments–cutting modernization timelines from years to months.

Let’s look at a few examples of how AWS Transform can help you through different aspects of the migration process.

Code analysis – AWS Transform provides comprehensive insights into your codebase, automatically examining mainframe codebases, creating detailed dependency graphs, measuring code complexity, and identifying component relationships

Documentation – AWS Transform for mainframe creates comprehensive technical and functional documentation of mainframe applications, preserving critical knowledge about features, program logic, and data flows. You can interact with the generated documentation through an AI-powered chat interface to discover and retrieve information quickly.

Business rule extraction – AWS Transform extracts and presents complex logic in plain language so you can gain visibility into business processes embedded within legacy applications. This enables both business and technical stakeholders to gain a greater understanding of application functionality.

Code decomposition – AWS Transform offers sophisticated code decomposition tools, including interactive dependency graphs and domain separation capabilities, enabling users to visualize and modify relationships between components while identifying key business functions. The solution also streamlines migration planning through an interactive wave sequence planner that considers user preferences to generate optimized migration strategies.

Modernization Wave Planning – With its specialized agent, AWS Transform for mainframe creates prioritized modernization wave sequences based on code and data dependencies, code volume, and business priorities. It enables modernization teams to make data-driven, customized migration plans that align to their specific organizational needs.

Code refactoring – AWS Transform can refactor millions of lines of mainframe code in minutes, converting COBOL, VSAM, and DB2 systems into modern Java Spring Boot applications while maintaining functional equivalence and transforming CICS transactions into web services and JCL batch processes into Groovy scripts. The solution provides high-quality output through configurable settings and bundled runtime capabilities, producing Java code that emphasizes readability, maintainability, and technical excellence.

Deployments – AWS Transform provides customizable deployment templates that streamline the deployment process through user-defined inputs. For added efficiency, the solution bundles the selected runtime version with the migrated application, enabling seamless deployment as a complete package.

By integrating intelligent documentation analysis, business rules extraction, and human-in-the-loop collaboration capabilities, AWS Transform helps organizations accelerate their mainframe transformation while reducing risk and maintaining business continuity.

VMware modernization
With rapid changes in VMware licensing and support model, organizations are increasingly exploring alternatives despite the difficulties associated with migrating and modernizing VMware workloads. This is aggravated by the fact that the accumulation of technical debt typically creates complex, poorly documented environments managed by multiple teams, leading to vendor lock-in and collaboration challenges that hinder migration efforts further.

AWS Transform is the first agentic AI service for VMware modernization of its kind that helps you to overcome those difficulties. It can offset risk and accelerate the modernization of VMware workloads by automating application discovery, dependency mapping, migration planning, network conversion, and EC2 instance optimization, reducing manual effort and accelerating cloud adoption.

The process is organized into four phases: inventory discovery, wave planning, network conversion, and server migration. It uses agentic AI capabilities to analyze and map complex VMware environments, converting network configurations into AWS built-in constructs and helps you to orchestrate dependency-aware migration waves for seamless cutovers. In addition, it also provides a collaborative web interface that keeps AWS teams, partners, and customers aligned throughout the modernization journey.

Let’s take a quick tour to see how this works.

Setting up
Before you can start using the service, you must first enable it by navigating to the AWS Transform console. AWS Transform requires AWS IAM Identity Center (IdC) to manage users and setup appropriate permissions. If you don’t yet have IdC set up it will ask you to configure it first and return to the AWS Transform console later to continue the process.

With IdC available, you can then proceed to choosing the encryption settings. AWS Transform gives you the option to use a default AWS managed key or you can use your own custom keys through AWS Key Management Service (AWS KMS).

After completing this step, AWS Transform will be enabled. You can manage admin access to the console by navigating to Users and using the search box to find them. You must create users or groups in IdC first if they don’t already exist. The service console will help admins provision users who will get access to the web app. Each provisioned user receives an email with a link to set password and get their personalized URL for the webapp.

You interact with AWS Transform through a dedicated web experience. To get the url, navigate to Settings where you can check your configurations and copy the links to the AWS Transform web experience where you and your teams can start using the service.

Discovery
AWS Transform can discover your VMware environment either automatically through AWS Application Discovery Service collectors or you can provide your own data by importing existing RVTools export files.

To get started, choose the Create or select connectors task and provide the account IDs for one or more AWS accounts that will be used for discovery. This will generate links that you can follow to authorize each account for usage within AWS Transform. You can then move on to the Perform discovery task, where you can choose to install AWS Application Discovery Service collectors or upload your own files such as exports from RVTools.

Provisioning
The steps for the provisioning phase are similar to the ones described earlier for discovery. You connect target AWS accounts by entering their account IDs and validating the authorization requests which will then enable the next steps such as the Generate VPC configuration step. Here, you can import your RVTools files or NSX exports from Import/Export from NSX, if applicable, and enable AWS Transform to understand your networking requirements.

You should then continue working through the job plan until you reach the point where it’s ready to deploy your Amazon Virtual Private Cloud (Amazon VPC). All the infrastructure as code (IaC) code is stored in Amazon Simple Storage Service (Amazon S3) buckets in the target AWS account.

Review the proposed changes and, if you’re happy, start the deployment process of the AWS resources to the target accounts.

Deployment
AWS Transform requires you to set up AWS Application Migration Service (MGN) in the target AWS accounts to automate the migration process. Choose the Initiate VM migration task and use the link to navigate to the service console, then follow the instructions to configure it.

After setting up service permissions, you’ll proceed to the implementation phase of the waves created by AWS Transform and start the migration process. For each wave, you’ll first be asked to make various choices such as setting the sizing preference and tenancy for the Amazon Elastic Compute Cloud (Amazon EC2) instances. Confirm your selections and continue following the instructions given by AWS Transform until you reach the Deploy replication agents stage, where you can start the migration for that wave.

After you start the waves migration process, you can switch to the dashboard at any time to check on progress.

With its agentic AI capabilities, AWS Transform offers a powerful solution for accelerating and de-risking mainframe and VMware modernization workloads. By automating complex assessment and transformation processes, AWS Transform reduces the time associated with legacy system migration while minimizing the potential for errors and business disruption enabling more agile, efficient, and future-ready IT environments within your organization.

Things to know
Availability –  AWS Transform for mainframe is available in US East (N. Virginia) and Europe (Frankfurt) Regions. AWS Transform for VMware offers different availability options for data collection and migrations. Please refer to the AWS Transform for VMware FAQ for more details.

Pricing –  Currently, we offer our core features—including assessment and transformation—at no cost to AWS customers.

Here are a few links for further reading.

Dive deeper into mainframe modernization and learn more about about AWS Transform for mainframe.

Explore more about VMware modernization and how to get started with your VMware migration journey.

Check out this interactive demo of AWS Transform for mainframe and this interactive demo of AWS Transform for VMware.

Matheus Guimaraes | @codingmatheus


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AWS Transform for .NET, the first agentic AI service for modernizing .NET applications at scale

Post Syndicated from Prasad Rao original https://aws.amazon.com/blogs/aws/aws-transform-for-net-the-first-agentic-ai-service-for-modernizing-net-applications-at-scale/

I started my career as a .NET developer and have seen .NET evolve over the last couple of decades. Like many of you, I also developed multiple enterprise applications in .NET Framework that ran only on Windows. I fondly remember building my first enterprise application with .NET Framework. Although it served us well, the technology landscape has significantly shifted. Now that there is an open source and cross-platform version of .NET that can run on Linux, these legacy enterprise applications built on .NET Framework need to be ported and modernized.

The benefits of porting to Linux are compelling: applications cost 40 percent less to operate because they save on Windows licensing costs, run 1.5–2 times faster with improved performance, and handle growing workloads with 50 percent better scalability. Having helped port several applications, I can say the effort is worth the rewards.

However, porting .NET Framework applications to cross-platform .NET is a labor-intensive and error-prone process. You have to perform multiple steps, such as analyzing the codebase, detecting incompatibilities, implementing fixes while porting the code, and then validating the changes. For enterprises, the challenge becomes even more complex because they might have hundreds of .NET Framework applications in their portfolio.

At re:Invent 2024, we previewed this capability as Amazon Q Developer transformation capabilities for .NET to help port your .NET applications at scale. The experience is available as a unified web experience for at-scale transformation and within your integrated development environment (IDE) for individual project and solution porting.

Now that we’ve incorporated your valuable feedback and suggestions, we’re excited to announce today the general availability of AWS Transform for .NET. We’ve also added new capabilities to support projects with private NuGet packages, port model-view-controller (MVC) Razor views to ASP .NET Core Razor views, and execute the ported unit tests.

I’ll expand on the key new capabilities in a moment, but let’s first take a quick look at the two porting experiences of AWS Transform for .NET.

Large-scale porting experience for .NET applications
Enterprise digital transformation is typically driven by central teams responsible for modernizing hundreds of applications across multiple business units. Different teams have ownership of different applications and their respective repositories. Success requires close coordination between these teams and the application owners and developers across business units. To accelerate this modernization at scale, AWS Transform for .NET provides a web experience that enables teams to connect directly to source code repositories and efficiently transform multiple applications across the organization. For select applications requiring dedicated developer attention, the same agent capabilities are available to developers as an extension for Visual Studio IDE.

Let’s start by looking at how the web experience of AWS Transform for .NET helps port hundreds of .NET applications at scale.

Web experience of AWS Transform for .NET
To get started with the web experience of AWS Transform, I onboard using the steps outlined in the documentation, sign in using my credentials, and create a job for .NET modernization.

Create a new job for .NET Transformation

AWS Transform for .NET creates a job plan, which is a sequence of steps that the agent will execute to assess, discover, analyze, and transform applications at scale. It then waits for me to set up a connector to connect to my source code repositories.

Setup connector to connect to source code repository

After the connector is in place, AWS Transform begins discovering repositories in my account. It conducts an assessment focused on three key areas: repository dependencies, required private packages and third-party libraries, and supported project types within your repositories.

Based on this assessment, it generates a recommended transformation plan. The plan orders repositories according to their last modification dates, dependency relationships, private package requirements, and the presence of supported project types.

AWS Transform for .NET then prepares for the transformation process by requesting specific inputs, such as the target branch destination, target .NET version, and the repositories to be transformed.

To select the repositories to transform, I have two options: use the recommended plan or customize the transformation plan by selecting repositories manually. For selecting repositories manually, I can use the UI or download the repository mapping and upload the customized list.

select the repositories to transform

AWS Transform for .NET automatically ports the application code, builds the ported code, executes unit tests, and commits the ported code to a new branch in my repository. It provides a comprehensive transformation summary, including modified files, test outcomes, and suggested fixes for any remaining work.

While the web experience helps accelerate large-scale porting, some applications may require developer attention. For these cases, the same agent capabilities are available in the Visual Studio IDE.

Visual Studio IDE experience of AWS Transform for .NET
Now, let’s explore how AWS Transform for .NET works within Visual Studio.

To get started, I install the latest version of AWS Toolkit extension for Visual Studio and set up the prerequisites.

I open a .NET Framework solution, and in the Solution Explorer, I see the context menu item Port project with AWS Transform for an individual project.

Context menu for Port project with AWS Transform in Visual Studio

I provide the required inputs, such as the target .NET version and the approval for the agents to autonomously transform code, execute unit tests, generate a transformation summary, and validate Linux-readiness.

Transformation summary after the project is transformed in Visual Studio

I can review the code changes made by the agents locally and continue updating my codebase.

Let’s now explore some of the key new capabilities added to AWS Transform for .NET.

Support for projects with private NuGet package dependencies 
During preview, only projects with public NuGet package dependencies were supported. With general availability, we now support projects with private NuGet package dependencies. This has been one of the most requested features during the preview.

The feature I really love is that AWS Transform can detect cross-repository dependencies. If it finds the source code of my private NuGet package, it automatically transforms that as well. However, if it can’t locate the source code, in the web experience, it provides me the flexibility to upload the required NuGet packages.

AWS Transform displays the missing package dependencies that need to be resolved. There are two ways to do this: I can either use the provided PowerShell script to create and upload packages, or I can build the application locally and upload the NuGet packages from the packages folder in the solution directory.

Upload packages to resolve missing dependencies

After I upload the missing NuGet packages, AWS Transform is able to resolve the dependencies. It’s best to provide both the .NET Framework and cross platform .NET versions of the NuGet packages. If the cross platform .NET version is not available, then at a minimum the .NET Framework version is required for AWS Transform to add it as an assembly reference and proceed for transformation.

Unit test execution
During preview, we supported porting unit tests from .NET Framework to cross-platform .NET. With general availability, we’ve also added support for executing unit tests after the transformation is complete.

After the transformation is complete and the unit tests are executed, I can see the results in the dashboard and view the status of the tests at each individual test project level.

Dashboard after successful transformation in web showing exectuted unit tests

Transformation visibility and summary
After the transformation is complete, I can download a detailed report in JSON format that gives me a list of transformed repositories, details about each repository, and the status of the transformation actions performed for each project within a repository. I can view the natural language transformation summary at the project level to understand AWS Transform output with project-level granularity. The summary provides me with an overview of updates along with key technical changes to the codebase.

detailed report of transformed project highlighting transformation summary of one of the project

Other new features
Let’s have a quick look at other new features we’ve added with general availability:

  • Support for porting UI layer – During preview, you could only port the business logic layers of MVC applications using AWS Transform, and you had to port the UI layer manually. With general availability, you can now use AWS Transform to port MVC Razor views to ASP.NET Core Razor views.
  • Expanded connector support – During preview, you could connect only to GitHub repositories. Now with general availability, you can connect to GitHub, GitLab, and Bitbucket repositories.
  • Cross repository dependency – When you select a repository for transformation, dependent repositories are automatically selected for transformation.
  • Download assessment report – You can download a detailed assessment report of the identified repositories in your account and private NuGet packages referenced in these repositories.
  • Email notifications with deep links – You’ll receive email notifications when a job’s status changes to completed or stopped. These notifications include deep links to the transformed code branches for review and continued transformation in your IDE.

Things to know
Some additional things to know are:

  • Regions – AWS Transform for .NET is generally available today in the Europe (Frankfurt) and US East (N. Virginia) Regions.
  • Pricing – Currently, there is no additional charge for AWS Transform. Any resources you create or continue to use in your AWS account using the output of AWS Transform will be billed according to their standard pricing. For limits and quotas, refer to the documentation.
  • .NET versions supported – AWS Transform for .NET supports transforming applications written using .NET Framework versions 3.5+, .NET Core 3.1, and .NET 5+, and the cross-platform .NET version, .NET 8.
  • Application types supported – AWS Transform for .NET supports porting C# code projects of the following types: console application, class library, unit tests, WebAPI, Windows Communication Foundation (WCF) service, MVC, and single-page application (SPA).
  • Getting started – To get started, visit AWS Transform for .NET User Guide.
  • Webinar – Join the webinar Accelerate .NET Modernization with Agentic AI to experience AWS Transform for .NET through a live demonstration.

– Prasad


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Enhanced remote desktop experience: Amazon DCV with Amazon Linux 2023

Post Syndicated from Madhur Kulkarni original https://aws.amazon.com/blogs/compute/enhanced-remote-desktop-experience-amazon-dcv-with-amazon-linux-2023/

Amazon DCV has evolved as a powerful remote display protocol, enabling secure high-performance remote desktop access and application streaming. This blog talks about how DCV remote display capabilities are now integrated with Amazon Linux 2023 (AL2023).

Overview

This post introduces new Graphical Desktop with AL2023 and provides an overview of new features available through DCV. The Graphical Desktop comes with GNOME 47 for a smooth UI experience that you can connect using DCV, enabling remote desktop access from anywhere. It also provides an overview of more tools such as a terminal emulator with Ptyxis for improved CLI experience, Mozilla Firefox for secure web browsing, an image viewer with Loupe, a text editor, and a file manager for file navigation, and.

Core features

AL2023 introduces an enhanced desktop experience, specifically tailored for remote access needs, as shown in the following Figure-1. DCV technology allows you to connect seamlessly to Graphical Desktop interface with GNOME 47. Users benefit from native DCV protocol support that enables high-performance remote access, featuring dynamic resolution adaptation and hardware-accelerated video encoding. Enhanced security features include advanced encryption and granular access controls.

Although DCV supports multiple desktop environments, the use of GNOME 47 is specifically part of the AL23 current release. The GNOME 47 system uses Mutter 47.0 as its window manager and compositor, alongside the GTK 4 toolkit for its user interface. This includes window management capabilities that provide more precise control over application placement and sizing, while improved multi-monitor support makes sure that workspaces expand seamlessly across displays. Most importantly, there is a native desktop-like experience with the DCV local features such as clipboard sharing, audio redirection, and multi-monitor capabilities, which deliver a seamless and responsive remote environment.

Figure 1. DCV desktop interface with AL2023

Ptyxis delivers exceptional performance with SSH, SFTP, and TLS/SSL support, as shown in the following figure. You can experience GPU-accelerated text rendering with a crystal-clear display, supporting UTF-8/16 and Unicode 15.0, while maintaining minimal input lag at 60 Hz refresh rates. The DCV 4K support enables high-resolution (3840 x 2160 pixels) remote desktop streaming, which allows users to work with graphically intensive applications while maintaining excellent visual quality. Ptyxis is deeply integrated with GNOME through D-Bus and GNOME I/O (GIO) interfaces, providing access to global search and system notifications. Users can use advanced session management with JSON-based configurations, tab groups, split views up to 16 panes, and automatic session restoration. The terminal includes full 256-color and true-color support, compatible with Bash, Zsh, and Fish shells, while maintaining robust connection stability.

Figure 2. Terminal Emulator

Firefox in AL2023 is optimized specifically for remote desktop scenarios, as shown in the following figure. The browser features hardware-accelerated rendering and WebGL 2.0 support, delivering smooth graphics and responsive page loading. Enhanced browser capabilities provide better performance for 3D applications and interactive web content. Users can experience optimized video playback with minimal frame drops and improved synchronization, which are particularly important for remote streaming needs. Integration with DCV streaming technology enables efficient resource usage and provides a local-like experience when accessing remote workstations, featuring seamless audio-video synchronization, smooth multi-monitor support, and native peripheral device integration.

Figure 3. Mozilla Firefox with DCV

More features

The GNOME Text Editor seamlessly integrates with AL2023, providing a modern, distraction-free interface for coding and text editing within the DCV environment, as shown in the following figure. As the default text editor in the AL23 GNOME desktop, it offers essential features such as syntax highlighting, dark/light themes, and autosave functionality, making it ideal for remote development work.

Figure 4. GNOME Text Editor

Loupe offers a sleek and intuitive image viewing experience in AL2023 when accessed through DCV, as shown in the following figure. It features a clean interface with smooth animations, efficient image loading, and gesture support, all while maintaining responsive performance over the DCV remote desktop connection. This makes it ideal for viewing and basic image manipulation tasks.

Figure 5. Image Viewer features and options

The GNOME File manager in AL2023 provides a robust and intuitive interface for managing files and folders when accessed through the DCV remote desktop environment, as shown in the following figure. It offers essential features such as drag-and-drop functionality, list and grid views, file search, and seamless integration with cloud storage, all while maintaining responsive performance over the DCV optimized remote connection protocol. You can use DCV to upload files to and download files from DCV session storage. For instructions on how to enable and configure session storage, go to Enabling session storage in the DCV Administrator Guide.

Figure 6. File Manager

Conclusion

The Amazon DCV team is committed to delivering the best remote desktop experience possible, and these enhancements demonstrate that commitment. In this post, we demonstrated how our integrated solution, from the GNOME 47 intuitive interface to the powerful terminal capabilities of Ptyxis, creates a seamless remote workspace. Using these improvements allows you to enhance productivity and overall user experience in remote desktop environments. These enhanced capabilities offer a significant step forward in remote computing, thereby providing tools and optimizations designed to meet the evolving needs of the distributed and flexible work environments today.

For a deeper dive into setup and advanced configurations, you should review our comprehensive DCV admin guides, which provide detailed information to help you maximize the potential of these new features.

AWS Weekly Roundup: South America expansion, Q Developer in OpenSearch, and more (May 12, 2025)

Post Syndicated from Micah Walter original https://aws.amazon.com/blogs/aws/aws-weekly-roundup-south-america-expansion-q-developer-in-opensearch-and-more-may-12-2025/

I’ve always been fascinated by how quickly we’re able to stand up new Regions and Availability Zones at AWS. Today there are 36 launched Regions and 114 launched Availability Zones. That’s amazing!

This past week at AWS was marked by significant expansion to our global infrastructure. The announcement of a new Region in the works for South America means customers will have more options for meeting their low latency and data residency requirements. Alongside the expansion, AWS announced the availability of numerous instance types in additional Regions.

In addition to the infrastructure expansion, AWS is also expanding the reach of Amazon Q Developer into Amazon OpenSearch Service.

Last week’s launches

Instance announcements

AWS expanded instance availability for an array of instance types across additional Regions.

Additional updates

Upcoming events

We are in the middle of AWS Summit season! AWS Summits run throughout the summer in cities all around the world. Be sure to check the calendar to find out when a AWS Summit is happening near you. Here are the remaining Summits for May, 2025.


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How to manage migration of hsm1.medium CloudHSM clusters to hsm2m.medium

Post Syndicated from Roshith Alankandy original https://aws.amazon.com/blogs/security/how-to-manage-migration-of-hsm1-medium-cloudhsm-clusters-to-hsm2m-medium/

On August 20, 2024, we announced the general availability of the new AWS CloudHSM instance type hsm2m.medium (hsm2). This new type comes with additional features compared to the previous AWS CloudHSM instance type, hsm1.medium (hsm1), such as support for Federal Information Processing Standard (FIPS) 140-3 Level 3, the ability to run clusters in non-FIPS mode, increased storage capacity of 16,666 total keys, and support for mutual transport layer security (mTLS) between the client and CloudHSM.

The hsm1 instance type is reaching end-of-life and will be unavailable for service on December 1, 2025. See the hsm1 deprecation notification.

To address this, starting April 2025, AWS will attempt to automatically migrate existing hsm1 clusters to hsm2. During the migration, the hsm1 cluster will operate in limited-write mode.

If you want to use automatic migration and can accommodate restrictions on operations during the migration, make sure that your environment meets the prerequisites for automatic migration.

If you want to manage the migration yourself, you can do so before the automatic migration begins. In this post, we provide a few options for migration so you can choose the method that’s best for your situation and available resources.

To help facilitate high availability during migration, you can use a blue/green deployment strategy. If high availability isn’t a priority, there are two approaches: one where write operations are restricted and a second where you incur some downtime on operations. We also cover different use cases based on the operations performed during migration and provide rollback strategies.

Important considerations

When planning a migration to hsm2, consider the following:

  • Backup: We recommend keeping a backup of hsm1 until you have confirmed that all the required keys have been migrated to hsm2. You can configure a CloudHSM backup retention policy to manage backups.

    Note: CloudHSM doesn’t delete a cluster’s last backup. See Configuring AWS CloudHSM backup retention policy for more information. You can also share the CloudHSM backups with other AWS accounts as described in Working with shared backups.

  • Availability and rollback: This post presents two main migration approaches. One that preserves availability but might become complex depending on the type of keys used and operations performed during the migration period. The other approach is less complicated but might impact availability for a short time. Choose the migration process based on your availability requirements.
  • Blue/Green strategy: You can use a blue/green deployment strategy using an enterprise-specific method or a CloudHSM multi-cluster configuration.

    Note: Multi-cluster configuration is supported for CloudHSM CLI, JCE, and PKCS11.

  • Client SDK version: Instance type hsm2 is compatible only with Client SDK version 5.9.0 and later. Upgrade your client SDK before starting migration. We recommend using the latest version.
  • Deprecated algorithms: Make sure you’re not using any deprecated algorithms. You won’t be able to migrate to an hsm2 cluster using backup if you’re using any deprecated algorithms. If you’re using 3DES, you can continue to use it in hsm2 non-FIPS clusters only. See How to migrate 3DES keys from a FIPS to a non-FIPS AWS CloudHSM cluster.
  • Known issues: See the known issues with hsm2 to amend your tests and metrics as needed after migration.

Limited availability

There are two options: customer triggered and customer managed. Choose the approach that best fits your requirements. Note that for both options, you need to satisfy the migration criteria. See Prerequisites for migrating to hsm2m.medium.

Customer triggered

You can trigger migration of your hsm1 cluster from the AWS Management Console for CloudHSM or the AWS Command Line Interface (AWS CLI), and AWS will manage the migration process. Follow the detailed steps in Migrating from hsm1.medium to hsm2m.medium. This approach is suitable if you don’t perform frequent write operations such as creating or deleting users or keys. During the migration, the hsm1 cluster enters limited-write mode where write operations will be rejected until migration is complete. Write operations performed by your application, if any, will fail during the migration. Read operations remain unaffected. If a rollback is required, it will be managed by AWS. If necessary, you can roll back the migration within 24 hours of starting it. The customer triggered migration process is straightforward because no configuration changes are required. If your application requires write operations during migration you can follow the customer managed option.

Customer managed

This approach is suitable if you can schedule a brief downtime to perform migration. For this process, you create a new hsm2 cluster using the latest hsm1 backup. After you add the same number of HSMs to the hsm2 cluster as are in the hsm1 cluster, stop the application, reconfigure the CloudHSM client library to hsm2, and restart the application.

  • Create an hsm2 cluster from backup: CloudHSM makes periodic backups of your cluster at least once every 24 hours. If you need a more recent backup, follow the steps in Cluster backups in AWS CloudHSM to trigger a backup. If you created a backup retention policy when you created the cluster, that will determine how long the backups are retained before being purged. The default is 90 days.

    After you have identified the backup, create an hsm2 cluster from the CloudHSM console or AWS CLI. For the console, choose HSM type hsm2m.medium and Cluster source as Restore cluster from existing backup and choose the designated backup of hsm1.

  • Update cluster for high availability: The new hsm2 cluster will have only one HSM instance. You can now add the same number of instances as hsm1 to this cluster. See adding an HSM to CloudHSM cluster. Based on your workload, add more HSMs to the cluster to ensure high availability. This is a good time to review the cluster to be sure that it follows best practices.
  • Reconfigure client SDKs: During the maintenance window, stop your application that is integrated with the CloudHSM client SDK, reconfigure the appropriate client SDK to talk to the new hsm2 cluster, and then restart the application. See Bootstrap the Client SDK to reconfigure the SDKs. An alternative to stopping and reconfiguring existing applications is to launch a new application instance with the CloudHSM client configured to talk to hsm2 and decommission the old application instance.
  • Monitor the application: Monitor your application’s health metrics and logs to verify that operations run against the new hsm2 cluster are successful. If you see increased errors, you can roll back to the hsm1 cluster and contact AWS Support for assistance.
  • Rollback: You can roll back by reconfiguring your application to communicate with the hsm1 cluster, similar to how you configured your application to talk to the hsm2 cluster.
  • Delete the hsm1 cluster: After you’re satisfied with your new hsm2 cluster, you can delete the hsm1 cluster to reduce costs. This action will create a backup that will be retained—CloudHSM doesn’t delete a cluster’s last backup.

High availability

If you need your CloudHSM cluster to be highly available during migration, AWS recommends that you follow the blue/green deployment methodology. The fundamental idea behind blue/green deployment is to shift traffic between two identical environments that are running different versions of a service or application. The blue environment represents the current version serving production traffic—the hsm1 cluster. The green environment is staged in parallel, running a different version of the service—an hsm2 cluster. After the green environment is ready and tested, production traffic is redirected from blue to green. If problems are identified, you can roll back by reverting traffic back to the blue environment.

We discuss two blue/green approaches in this post. Approach 1 uses a load balancer to route traffic between the blue and green configurations. Approach 2 uses CloudHSM multi-cluster configuration and requires application code changes. Each has pros and cons in terms of effort and cost.

If you have already implemented a multi-cluster configuration in your application, you can follow Approach 2; otherwise, we recommend Approach 1.

A few important things to keep in mind when you implement either of these approaches.

  • You need to create the hsm2 cluster from the hsm1 backup as described in Customer managed.
  • If you need to support write operations during migration, you will need to run additional processes to make sure the data is in sync between the blue and green clusters. See Use cases to learn about different scenarios and plan accordingly.

Approach 1

For this approach, you create two separate but identical client environments. One environment (blue) runs the current application and the client SDK that connects to the hsm1 cluster. The other environment (green) runs the same application with the client SDK configured to talk to the hsm2 cluster. You then use a load balancer—such as Application Load Balancer (ALB)—to selectively route traffic between blue and green using the weighted target groups routing feature of ALB or an equivalent feature in your load balancer.

You can start by directing a small percentage of your application traffic to green. When you’re confident that green is performing well and is stable, shift traffic to green and shut down blue.

Figure 1: Blue/green migration architecture

Figure 1: Blue/green migration architecture

The following are the steps of the migration architecture shown in Figure 1:

  1. Create an hsm2 cluster from an hsm1 backup as described in Customer managed. Make sure you create the new cluster in the same Availability Zones as the existing CloudHSM cluster. This will be your green environment.
  2. Spin up new application instances in the green environment and configure them to connect to the new hsm2 cluster.
  3. Add the new client instances to a new target group for the ALB.
  4. Next, use the weighted target groups routing feature of ALB to route traffic to the newly configured environment.
    1. Each target group weight is a value from 0 to 999. Requests that match a listener rule with weighted target groups are distributed to these target groups based on their weights.
    2. For more information, see Fine-tuning blue/green deployments on application load balancer.

You can follow the canary deployment pattern to roll out an hsm2 cluster integrated application to a subset of users before making it widely available while the hsm1 integrated application serves most of the users. To start, you can configure blue target group with a weight of 90 and green with 10; the ALB will route 90 percent of the traffic to the blue target group and 10 percent to green.

Monitor applications to verify that operations to green are successful (see Monitoring). After you’re satisfied with the response from green, you can update the weights to 0 and 100 for blue and green to completely switch over to green and then shut down blue.

For alternate approaches, such as DNS weighted distribution, see Blue/Green Deployments on AWS

Approach 2

This approach uses a single application environment that talks to both the hsm1 and hsm2 clusters. To shift traffic between blue and green environments, you will use the CloudHSM multi-cluster configuration, which allows a single client SDK to communicate with two or more CloudHSM clusters. Your application code needs to be modified to communicate with both blue and green clusters. In this post, we use a JCE SDK multi-cluster configuration, shown in Figure 2 that follows.

Figure 2: Multi-cluster migration architecture

Figure 2: Multi-cluster migration architecture

The solution uses the basic blue/green deployment steps using a multi-cluster configuration and is designed for common use cases based on the type of CloudHSM operations performed during migration. We also cover how keys can be synchronized between the blue and green clusters and how to roll back.

Create an hsm2 cluster from an hsm1 backup

As described in Customer managed, create an hsm2 cluster from an hsm1 backup. Make sure you create the new cluster in the same Availability Zones as the existing CloudHSM cluster. This will be your green environment.

Modify the application to talk to both blue and green

In this step, you modify the application to use multi-cluster configuration to talk to both blue and green. When using a multi-cluster configuration, you need to configure the CloudHSM provider in the code instead of using the default config file.

In the application code, instantiate two providers: providerHsm1 pointing to blue cluster and providerHsm2 pointing to green cluster. Then update the business logic to switch traffic between blue and green using these providers.

  • Instantiate providers as shown in the following example. See Connecting to multiple clusters with CloudHSM CLI for a detailed explanation. Replace the following:
    • <hsmCAFilePath>: File path to hsm1 trust anchor certificate that you used to initialize the cluster.
    • <hsm1ClusterID>: The unique cluster ID of the hsm1 cluster.
    • <hsm2ClusterID>: The unique cluster ID of the hsm2 cluster.
    CloudHsmProviderConfig hsm1Config = CloudHsmProviderConfig.builder() 
    .withCluster( 
    CloudHsmCluster.builder() 
    .withHsmCAFilePath(<hsmCAFilePath>)
    .withClusterUniqueIdentifier("<hsm1ClusterID>")
    .withServer(CloudHsmServer.builder().withHostIP(hsm1HostName).build()) 
    .build()) 
    .build();
    CloudHsmProvider providerHsm1 = new CloudHsmProvider(hsm1Config);
    
       if (Security.getProvider(provider1.getName()) == null) {.  
                     Security.addProvider(provider1);
         }
    
    CloudHsmProviderConfig hsm2Config = CloudHsmProviderConfig.builder() 
    .withCluster( 
    CloudHsmCluster.builder() 
    .withHsmCAFilePath(<hsmCAFilePath>)
    .withClusterUniqueIdentifier("<sm2ClusterID>")
    .withServer(CloudHsmServer.builder().withHostIP(hsm2HostName).build()) 
    .build()) 
    .build();
    
    CloudHsmProvider providerHsm2 = new CloudHsmProvider(hsm2Config);
    
    if (Security.getProvider(provider2.getName()) == null) { 
                  Security.addProvider(provider2);
    }
    

  • Direct operations to blue and green using the respective providers.
    Cipher cipher1 = Cipher.getInstance("AES/GCM/NoPadding", providerHsm1);
    
    Cipher cipher2 = Cipher.getInstance("AES/GCM/NoPadding", providerHsm2);
    

Switch to green and shut down blue

Monitor the application to verify that operations on green are successful. See the Monitoring section. Once you are satisfied with response from green, you can update the application code to completely switch over to green.

Monitoring

During migration to hsm2, it’s important to monitor your application to confirm it’s working as expected and roll back if you notice increased errors. You can use your application logs and the CloudHSM client SDK logs to monitor the application.

Note: There are some known issues with hsm2 that will be fixed in future releases. See Known issues for AWS CloudHSM hsm2m.medium instances for a list of current known issues and their resolution status.

Use cases

Depending on the type of operations you perform on your CloudHSM cluster during migration, you need to run additional processes to make sure the data is in sync between the blue and green clusters. This will help avoid the split-brain scenario where blue and green clusters are in an inconsistent state if a write operation is performed during migration.

Read-only operations

During migration, if you only need to perform read operations—meaning you aren’t creating token keys—then the data between the clusters will be consistent. You can switch over to green completely following the blue/green-deployment methodology in Approach 1 or Approach 2.

Create/delete operations

If token keys need to be created during migration, the blue and green clusters need to be synchronized to make sure that read operations to the clusters are successful.

  • Write to blue: Initially, create operations can be directed to blue and read operations to both blue and green. In this case, the newly created keys need to be replicated to green. You can use the CloudHSM CLI key replicate command to synchronize keys. See Replicate keys.
  • Write to green: After you gain confidence in the read capability of the green cluster, you could begin swapping over the application to do write operations against the green cluster. In this case, if you’re still reading from both blue and green, you can replicate keys to blue using the CloudHSM CLI key replicate. See Replicate keys.

Replicate keys

Keys can be replicated between CloudHSM clusters that are created from the same backup using CloudHSM CLI with multi-cluster configuration.

Step 1: Configure multi-cluster:

Add blue and green clusters to the multi-cluster configuration. See Connecting to multiple clusters with CloudHSM CLI.

Step 2: Replicate keys from source to destination

Make sure that key owners and users that the key is shared with exist in the destination. Also, the crypto user or admin performing the operation needs to sign in to both clusters.

Run the key replicate command to replicate the keys from blue to green or vice versa as shown in the following example.

  • List keys in hsm1:
    crypto_user@cluster-<hsm1ClusterID> > key list --cluster-id cluster-<hsm1ClusterID>
    

  • List keys in hsm2:
    crypto-user@cluster-<hsm1ClusterID> > key list --cluster-id cluster-<hsm2ClusterID>
    

  • Replicate keys:
    crypto_user@cluster-<hsm1ClusterID> > key replicate \
    --filter attr.label=example-aes-2 \
    --source-cluster-id cluster-<hsm1ClusterID> \
    --destination-cluster-id cluster-<hsm2ClusterID>
    

Rollback

The complexity of a rollback will depend on the stage of the migration and what keys were created. Normally, whether it’s during the migration or after, if you aren’t using hsm2-specific features such as new key attributes, then the rollback is straightforward. During the migration, if a rollback is needed, you can point your application back toward the hsm1 cluster. Through this approach, reads and writes will revert to happening on just the hsm1 and the rollback will be complete. If you created keys in only hsm2, you can replicate them back to hsm1.

The other scenario for a rollback is if you cannot replicate keys back to the hsm1 cluster. This can happen if you have fully migrated your application to hsm2 and have created more than 3,300 keys (the limit for hsm1) or are using hsm2-specific features. In this scenario, you need to make application changes to return to a multi-cluster setup where reads are performed against both hsm1 and hsm2 clusters (in case the keys exist in only hsm2), but write operations happen solely on the hsm1. In this case, the recommendation is to continue talking to both clusters and keep them in sync until non-replicable keys are no longer needed and the cluster can be scaled back down.

Conclusion

In this post, we described strategies to migrate a hsm1.medium CloudHSM cluster to hsm2m.medium. We explored commonly used blue/green deployments and AWS CloudHSM provided options. We also explored common use cases, steps to avoid common pitfalls, and rollback options.

If you have feedback about this post, submit comments in the Comments section below. If you have questions about this post, contact AWS Support.

Roshith Alankandy

Roshith is a Security Consultant at AWS, based in Australia. He helps customers accelerate their cloud adoption journey with security, risk, and compliance guidance and specializes in cryptography. When not working, he enjoys spending time with his family and playing football.

AWS expands Spain’s ENS High certification across 174 services

Post Syndicated from Daniel Fuertes original https://aws.amazon.com/blogs/security/aws-expands-spains-ens-high-certification-across-174-services/

Amazon Web Services (AWS) has successfully renewed its Esquema Nacional de Seguridad (ENS) High certification under the latest framework established by Royal Decree 311/2022. This achievement demonstrates the continued dedication of AWS to meeting the stringent security requirements essential for serving Spanish government entities and public organizations.

The ENS framework serves as the cornerstone of cybersecurity standards for Spain’s public sector. It establishes comprehensive security requirements for government agencies, public organizations, and service providers supporting Spanish public services. The framework implements a tiered security approach, with three distinct levels (Basic, Medium, and High), each level requiring progressively stringent security measures and controls.

By maintaining and expanding our ENS certification at its High level, AWS reaffirms its commitment to providing secure cloud services that meet compliance standards and the evolving needs of Spain’s public sector and its technology partners.

For organizations working with Spanish public administration, this expanded certification offers significant advantages. Customers can operate with reliable compliance with Spain’s highest security standards while accessing a broader range of certified cloud services. This certification provides enhanced confidence in their cloud security posture and enables streamlined procurement processes for public sector projects.

With this renewal, AWS has broadened its ENS-certified portfolio. The certification now encompasses 8 additional services, bringing the total to 174 AWS ENS-certified services. This extensive coverage spans across 31 AWS Regions (including Spain), providing customers with unprecedented access to certified cloud services. Some of the additional services in scope for ENS High include the following:

  • Amazon DataZone – This data management service makes it faster and more straightforward for customers to catalog, discover, share, and govern data stored across AWS, on premises, and third-party sources.
  • AWS AppFabric – This service natively connects software as a service (SaaS) applications across organizations. It normalizes application data for administrators to set common policies.
  • AWS Resilience Hub – A central location in the AWS Console that helps customers to manage and improve the resilience posture of their applications on AWS.
  • AWS User Notifications – A centralized view of notifications from AWS services, across accounts, Regions, and services, including Amazon CloudWatch alarms or Amazon Elastic Compute Cloud (Amazon EC2) instance state changes, in a consistent, human-friendly format.

AWS achievement of the ENS High recertification is verified by an accredited company, which conducted an independent audit and confirmed that AWS continues to adhere to the confidentiality, integrity, and availability standards at the highest level as described in Royal Decree 311/2022.

For more information about ENS High, see the AWS Compliance page Esquema Nacional de Seguridad High. To view the complete list of services included in the scope, see the AWS Services in Scope by Compliance Program – Esquema Nacional de Seguridad (ENS) page. You can download the ENS High Certificate from AWS Artifact in the AWS Management Console or from Esquema Nacional de Seguridad High.

As always, we are committed to bringing new services into the scope of our ENS High program based on your architectural and regulatory needs. If you have questions about the ENS program, reach out to your AWS account team or contact AWS Compliance.

If you have feedback about this post, submit comments in the Comments section below.

Daniel Fuertes

Daniel Fuertes

Daniel is a Security Audit Program Manager at AWS based in Madrid, Spain. Daniel leads multiple security audits, attestations, and certification programs in Spain and other EMEA countries. He has twelve years of experience in security assurance and compliance, including previous experience as an auditor for the PCI DSS security framework. He also holds the CISSP, PCIP, and ISO 27001 Lead Auditor certifications.

AWS renews its AAA Pinakes rating for the Spanish financial sector

Post Syndicated from Daniel Fuertes original https://aws.amazon.com/blogs/security/aws-renews-its-aaa-pinakes-rating-for-the-spanish-financial-sector/

Amazon Web Services (AWS) has successfully revalidated its prestigious AAA rating under the Pinakes qualification system, with certification coverage extending to 174 services across 31 global AWS Regions. This achievement marks a significant milestone in the commitment of AWS to serving the Spanish financial sector with the highest security standards and assurance.

The Pinakes framework, developed by the Centro de Cooperación Interbancaria (CCI), stands as a comprehensive security rating system designed to evaluate and monitor service providers working with Spanish financial institutions. This sophisticated framework encompasses 1,315 requirements, strategically organized into four fundamental categories: confidentiality, integrity, availability of information, and general requirements.

The framework’s evaluation spans 14 domains, encompassing:

  • Information security management program
  • Third-party management
  • Normative compliance
  • Network controls
  • Access controls
  • Incident management
  • Encryption
  • Secure development
  • Continuous Monitoring
  • Antimalware protection
  • Resilience
  • Systems operation
  • Personnel security
  • Facilities security

Pinakes implements a sophisticated rating scale ranging from A+ to D, where A+ represents the highest level of cybersecurity management implementation, and D indicates compliance with minimum security requirements. Each requirement undergoes thorough evaluation by an independent third-party auditor, providing objective assessment of security measures.

The renewal of AWS A ratings across confidentiality, integrity, and availability domains, culminating in an overall AAA security rating, demonstrates our ongoing investment in meeting industry benchmarks. This achievement validates our robust security controls and underscores our dedication to protecting the interests of our Spanish financial sector customers.

This requalification reaffirms the position AWS holds as a trusted service provider and highlights our continuous commitment to maintaining and enhancing our security posture in the Spanish financial sector.

The full control matrix will be published on AWS Artifact and available on request. Pinakes participants who are AWS customers can contact their AWS account manager to request access to it.

As always, we value your feedback and questions. Reach out to the AWS Compliance team through the Contact Us page. To learn more about our other compliance and security programs, see AWS Compliance Programs.

If you have feedback about this post, submit it in the Comments section below.

Daniel Fuertes

Daniel Fuertes

Daniel is a Security Audit Program Manager at AWS based in Madrid, Spain. Daniel leads multiple security audits, attestations, and certification programs in Spain and other EMEA countries. He has twelve years of experience in security assurance and compliance, including previous experience as an auditor for the PCI DSS security framework. He also holds the CISSP, PCIP, and ISO 27001 Lead Auditor certifications.

Introducing the AWS User Guide to Governance, Risk and Compliance for Responsible AI Adoption within Financial Services Industries

Post Syndicated from Krish De original https://aws.amazon.com/blogs/security/introducing-the-aws-user-guide-to-governance-risk-and-compliance-for-responsible-ai-adoption-within-financial-services-industries/

Financial services institutions (FSIs) are increasingly adopting AI technologies to drive innovation and improve customer experiences. However, this adoption brings new governance, risk, and compliance (GRC) considerations that organizations need to address. To help FSI customers navigate these challenges, AWS is excited to announce the launch of the AWS User Guide to Governance, Risk and Compliance for Responsible AI Adoption within Financial Services Industries.

This comprehensive guide provides FSI customers practical considerations for responsible AI adoption across key dimensions including governance, risk management, compliance, data management, model management and AI agent management. It includes detailed AWS service capabilities that customers can use to address these considerations, such as Amazon Bedrock Guardrails, Amazon Bedrock Agents, Amazon SageMaker Autopilot, and Amazon SageMaker Model Monitor.

The guide is available through AWS Artifact and is complementary to other AWS resources such as the AWS Responsible Use of AI Guide, AWS Cloud Adoption Framework for AI, AWS Well-Architected Framework Generative AI Lens, and AWS Well-Architected Framework Machine Learning Lens.

As the regulatory environment and leading practices continue to evolve, we’ll provide further updates on the AWS Security Blog and AWS Compliance Center. You can also reach out to your AWS account team for help finding the resources you need.

 
If you have feedback about this post, submit comments in the Comments section below. If you have questions about this post, contact AWS Support.

Krish De
Krish De

Krish is a Principal FSI Governance, Risk & Compliance (GRC) specialist. He works with AWS customers, their regulators, and AWS teams to safely accelerate customers’ cloud adoption by providing prescriptive guidance on GRC. Krish has over 20 years of experience working in governance, risk, and technology across the financial services industry in Australia, New Zealand, and the United States.
Brenda Fong
Brenda Fong

Brenda is a senior FSI risk and compliance specialist. She works with AWS customers in banking, insurance, and capital markets within the ASEAN region to help them meet regulatory, governance, risk, and compliance expectations. Brenda has over 20 years of experience working in governance, risk, and technology across the financial services industry within Asia Pacific.
Kelvin Leung
Stephen Martin

Steve is the Head of Financial Services Compliance and Security for EMEA and APAC. Steve Joined AWS after working for over 20 years in financial service in senior leadership roles with responsibility across ASIA, the Middle East, and Europe. At AWS, he supports customers as they use the scale, security, and agility of AWS to transform the industry.
Kelvin Leung
Kelvin Leung

Kelvin is the AWS FSI Security and Compliance Lead based in Hong Kong. He has 20 years of experience in the IT and cloud regulatory space, with a focus on IT outsourcing, information security, and compliance. Prior to joining AWS, Kelvin worked for a financial regulator where he was responsible for technology risk policy-making and IT regulatory examinations.

In the works – AWS South America (Chile) Region

Post Syndicated from Elizabeth Fuentes original https://aws.amazon.com/blogs/aws/coming-soon-aws-south-america-chile-region/

Today, Amazon Web Services (AWS) announced plans to launch a new AWS Region in Chile by the end of 2026. The AWS South America (Chile) Region will consist of three Availability Zones at launch, bringing AWS infrastructure and services closer to customers in Chile. This new Region joins the AWS South America (São Paulo) and AWS Mexico (Central) Regions as our third AWS Region in Latin America. Each Availability Zone is separated by a meaningful distance to support applications that need low latency while significantly reducing the risk of a single event impacting availability.

Skyline of Santiago de Chile with modern office buildings in the financial district in Las Condes

The new AWS Region will bring advanced cloud technologies, including artificial intelligence (AI) and machine learning (ML), closer to customers in Latin America. Through high-bandwidth, low-latency network connections over dedicated, fully redundant fiber, the Region will support applications requiring synchronous replication while giving you the flexibility to run workloads and store data locally to meet data residency requirements.

AWS in Chile
In 2017, AWS established an office in Santiago de Chile to support local customers and partners. Today, there are business development teams, solutions architects, partner managers, professional services consultants, support staff, and personnel in various other roles working in the Santiago office.

As part of our ongoing commitment to Chile, AWS has invested in several infrastructure offerings throughout the country. In 2019, AWS launched an Amazon CloudFront edge location in Chile. This provides a highly secure and programmable content delivery network that accelerates the delivery of data, videos, applications, and APIs to users worldwide with low latency and high transfer speeds.

AWS strengthened its presence in 2021 with two significant additions. First, an AWS Ground Station antenna location in Punta Arenas, offering a fully managed service for satellite communications, data processing, and global satellite operations scaling. Second, AWS Outposts in Chile, bringing fully managed AWS infrastructure and services to virtually any on-premises or edge location for a consistent hybrid experience.

In 2023, AWS further enhanced its infrastructure with two key developments, an AWS Direct Connect location in Chile that lets you create private connectivity between AWS and your data center, office, or colocation environment, and AWS Local Zones in Santiago, placing compute, storage, database, and other select services closer to large population centers and IT hubs. The AWS Local Zone in Santiago helps customers deliver applications requiring single-digit millisecond latency to end users.

The upcoming AWS South America (Chile) Region represents our continued commitment to fueling innovation in Chile. Beyond building infrastructure, AWS plays a crucial role in developing Chile’s digital workforce through comprehensive cloud education initiatives. Through AWS Academy, AWS Educate, and AWS Skill Builder, AWS provides essential cloud computing skills to diverse groups—from students and developers to business professionals and emerging IT leaders. Since 2017, AWS has trained more than two million people across Latin America on cloud skills, including more than 100,000 in Chile.

AWS customers in Chile
AWS customers in Chile have been increasingly moving their applications to AWS and running their technology infrastructure in AWS Regions around the world. With the addition of this new AWS Region, customers will be able to provide even lower latency to end users and use advanced technologies such as generative AI, Internet of Things (IoT), mobile services, banking industry, and more, to drive innovation. This Region will give AWS customers the ability to run their workloads and store their content in Chile.

Here are some examples of customers in Chile using AWS to drive innovation:

The Digital Government Secretariat (SGD) is the Chilean government institution responsible for proposing and coordinating the implementation of the Digital Government Strategy, providing an integrated government approach. SGD coordinates, advises, and provides cross-sector support in the strategic use of digital technologies, data, and public information to improve state administration and service delivery. To fulfill this mission, SGD relies on AWS to operate critical digital platforms including Clave Única (single sign-on), FirmaGob (digital signature), the State Electronic Services Integration Platform (PISEE), DocDigital, SIMPLE, and the Administrative Procedures and Services Catalog (CPAT), among others.

Transbank, Chile’s largest payment solutions ecosystem managing the largest percentage of national transactions, used AWS to significantly reduce time-to-market for new products. Moreover, Transbank implemented multiple AWS-powered solutions, enhancing team productivity and accelerating innovation. These initiatives showcase how financial technology companies can use AWS to drive innovation and operational efficiency. “The new AWS Region in Chile will be very important for us,” said Jorge Rodríguez M., Chief Architecture and Technology Officer (CA&TO) of Transbank. “It will further reduce latency, improve security and expand the possibilities for innovation, allowing us to serve our customers with new and better services and products.”

To learn more about AWS customers in Chile, visit AWS Customer Success Stories.

AWS sustainability efforts in Chile
AWS is committed to water stewardship in Chile through innovative conservation projects. In the Maipo Basin, which provides essential water for the Metropolitan Santiago and Valparaiso regions, AWS has partnered with local farmers and climate-tech company Kilimo to implement water-saving initiatives. The project involves converting 67 hectares of agricultural land from flood to drip irrigation, which will save approximately 200 million liters of water annually.

This water conservation effort supports AWS commitment to be water positive by 2030 and demonstrates our dedication to environmental sustainability in the communities where AWS operate. The project uses efficient drip irrigation systems that deliver water directly to plant root systems through a specialized pipe network, maximizing water efficiency for agricultural use. To learn more about this initiative, read our blog post AWS expands its water replenishment program to China and Chile—and adds projects in the US and Brazil.

AWS community in Chile
The AWS community in Chile is one of the most active in the region, comprising of AWS Community Builders, two AWS User Groups (AWS User Group Chile and AWS Girls Chile), and an AWS Cloud Club. These groups hold monthly events and have organized two AWS Community Days. At the first Community Day, held in 2023, we had the honor of having Jeff Barr as the keynote speaker.

Chile AWS Community Day 2023

Stay tuned
We’ll announce the opening of this and the other Regions in future blog posts, so be sure to stay tuned! To learn more, visit the AWS Region in Chile page.

Eli

Thanks to Leonardo Vilacha for the Chile AWS Community Day 2023 photo.


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Accelerate the transfer of data from an Amazon EBS snapshot to a new EBS volume

Post Syndicated from Channy Yun (윤석찬) original https://aws.amazon.com/blogs/aws/accelerate-the-transfer-of-data-from-an-amazon-ebs-snapshot-to-a-new-ebs-volume/

Today we are announcing the general availability of Amazon Elastic Block Store (Amazon EBS) Provisioned Rate for Volume Initialization, a feature that accelerates the transfer of data from an EBS snapshot, a highly durable backup of volumes stored in Amazon Simple Storage Service (Amazon S3) to a new EBS volume.

With Amazon EBS Provisioned Rate for Volume Initialization, you can create fully performant EBS volumes within a predictable amount of time. You can use this feature to speed up the initialization of hundreds of concurrent volumes and instances. You can also use this feature when you need to recover from an existing EBS Snapshot and need your EBS volume to be created and initialized as quickly as possible. You can use this feature to quickly create copies of EBS volumes with EBS Snapshots in a different Availability Zone, AWS Region, or AWS account. Provisioned Rate for Volume Initialization for each volume is charged based on the full snapshot size and the specified volume initialization rate.

This new feature expedites the volume initialization process by fetching the data from an EBS Snapshot to an EBS volume at a consistent rate that you specify between 100 MiB/s and 300 MiB/s. You can specify this volume initialization rate at which the snapshot blocks are to be downloaded from Amazon S3 to the volume.

With specifying the volume initialization rate, you can create a fully performant volume in a predictable time, enabling increased operational efficiency and visibility on the expected time of completion. If you run utilities like fio/dd to expedite volume initialization for your workflows like application recovery and volume copy for testing and development, it will remove the operational burden of managing such scripts with the consistency and predictability to your workflows.

Get started with specifying the volume initialization rate
To get started, you can choose the volume initialization rate when you launch your EC2 instance or create your volume from the snapshot.

1. Create a volume in the EC2 launch wizard
When launching new EC2 instances in the launch wizard of EC2 console, you can enter a desired Volume initialization rate in the Storage (volumes) section.

You can also set the volume initialization rate when creating and modifying the EC2 Launch Templates.

In the AWS Command Line Interface (AWS CLI), you can add VolumeInitializationRate parameter to the block device mappings when call run-instances command.

aws ec2 run-instances \
    --image-id ami-0abcdef1234567890 \
    --instance-type t2.micro \
    --subnet-id subnet-08fc749671b2d077c \
    --security-group-ids sg-0b0384b66d7d692f9 \
    --key-name MyKeyPair \
    --block-device-mappings file://mapping.json

Contents of mapping.json. This example adds /dev/sdh an empty EBS volume with a size of 8 GiB.

[
    {
        "DeviceName": "/dev/sdh",
        "Ebs": {
            "VolumeSize": 8
            "VolumeType": "gp3",            
            "VolumeInitializationRate": 300
		 } 
     } 
]

To learn more, visit block device mapping options, which defines the EBS volumes and instance store volumes to attach to the instance at launch.

2. Create a volume from snapshots
When you create a volume from snapshots, you can also choose Create volume in the EC2 console and specify the Volume initialization rate.

Confirm your new volume with the initialization rate.

In the AWS CLI, you can use VolumeInitializationRate parameter and when calling create-volume command.

aws ec2 create-volume --region us-east-1 --cli-input-json '{
    "AvailabilityZone": "us-east-1a",
    "VolumeType": "gp3",
    "SnapshotId": "snap-07f411eed12ef613a",
    "VolumeInitializationRate": 300
}'

If the command is run successfully, you will receive the result below.

{
    "AvailabilityZone": "us-east-1a",
    "CreateTime": "2025-01-03T21:44:53.000Z",
    "Encrypted": false,
    "Size": 100,
    "SnapshotId": "snap-07f411eed12ef613a",
    "State": "creating",
    "VolumeId": "vol-0ba4ed2a280fab5f9",
    "Iops": 300,
    "Tags": [],
    "VolumeType": "gp2",
    "MultiAttachEnabled": false,
    "VolumeInitializationRate": 300
}

You can also set the volume initialization rate when replacing root volumes of EC2 instances and provisioning EBS volumes using the EBS Container Storage Interface (CSI) driver.

After creation of the volume, EBS will keep track of the hydration progress and publish an Amazon EventBridge notification for EBS to your account when the hydration completes so that they can be certain when their volume is fully performant.

To learn more, visit Create an Amazon EBS volume and Initialize Amazon EBS volumes in the Amazon EBS User Guide.

Now available
Amazon EBS Provisioned Rate for Volume Initialization is now available and supported for all EBS volume types today. You will be charged based on the full snapshot size and the specified volume initialization rate. To learn more, visit Amazon EBS Pricing page.

To learn more about Amazon EBS including this feature, take the free digital course on the AWS Skill Builder portal. Course includes use cases, architecture diagrams and demos.

Give this feature a try in the Amazon EC2 console today and send feedback to AWS re:Post for Amazon EBS or through your usual AWS Support contacts.

— Channy


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Introducing the AWS Zero Trust Accelerator for Government

Post Syndicated from Derek Doerr original https://aws.amazon.com/blogs/security/introducing-the-aws-zero-trust-accelerator-for-government/

Government agencies face an unprecedented challenge when designing security against unauthorized access to IT infrastructure and data. Traditional perimeter-based security models—which rely on the assumption of trust within an organization’s network boundaries—are no longer sufficient. The wide adoption of bring-your-own-device (BYOD) and cloud-based resources requires adopting additional security measures beyond the traditional perimeter-based models. High-profile cyber incidents, such as the Global exploit of the JetBrains CVE and the compromise of federal networks by Iranian government-sponsored APT actors, highlight the limitations of traditional perimeter-based security models.

Recognizing the urgency of this challenge, the Biden administration issued Executive Order 14028, “Improving the Nation’s Cybersecurity,” in May 2021. This executive order mandates US federal agencies to adopt zero trust architectures (ZTAs) to strengthen their cybersecurity posture and protect critical infrastructure from cyber threats. Additionally, the Department of Defense (DoD) and the Cybersecurity and Infrastructure Security Agency (CISA) have published comprehensive guidance on implementing zero trust principles, including the DoD Zero Trust Strategy and the CISA Zero Trust Maturity Model. The US Office of Management and Budget (OMB) has set targets for Federal Civilian Executive Branch (FCEB) agencies to implement CISA guidance in FY2024 and FY2025, while DoD has set targets for FY2027 and beyond.

Zero trust principles focus on authorizing access to protected resources such as data, applications, and services, by continuously verifying the identity and security posture of every user, device, and transaction, regardless of network location. This approach aims to reduce the concept of implicit trust, verifying that only authorized entities gain access to sensitive resources and reducing the risks associated with unauthorized access and lateral movement within the network.

Amazon Web Services (AWS) is at the forefront of this paradigm shift, offering a government-centric suite of services and capabilities to support government agencies in their transition to a zero trust approach. The zero trust approach recommended by AWS is designed to provide a robust, scalable, and forward-looking cybersecurity strategy that aligns with government mandates and empowers agencies to secure their mission-critical resources effectively.

The AWS ZTAG: A government-centric approach

The AWS Zero Trust Accelerator for Government (ZTAG) is a government-centric set of resources to help government organizations implement zero trust architectures. ZTAG encompasses several accelerators, including:

  • Zero trust maturity assessment tools
  • Reference architectures and implementation guidance
  • Integration of AWS services and AWS Independent Software Vendor (ISV) partner solutions
  • AWS ISV reference implementations with industry-leading ISV partners
  • A streamlined procurement process through AWS Marketplace

The ZTAG assessment tools help you identify gaps in adhering to government zero trust requirements and provide tailored guidance and recommendations. This includes AWS services and AWS ISV partner solutions designed to help you achieve specific US DoD zero trust activities or CISA zero trust functions. ZTAG is initially focused on US government zero trust frameworks with applicability at the federal, state, and local levels, with adoption of international zero trust frameworks on the roadmap.

Accelerating zero trust adoption with AWS

The ZTAG approach is specifically tailored to help meet the unique requirements and challenges faced by government agencies, offering several key benefits:

  • Aligns with US DoD and CISA zero trust models and is extensible to other government or industry models as they emerge
  • Accelerates your journey to a secure and resilient IT infrastructure by helping you identify zero trust gaps and define roadmaps to achieve cybersecurity objectives
  • Starts with your existing cyber capabilities and extends them as needed with best-of-breed AWS ISV partners
  • Incremental approach to adoption enables smooth transition to a zero trust architecture
  • Dedicated expertise to assist government agencies throughout their zero trust journey

Getting started with ZTAG

To get started with their zero trust journey, government agencies can use AWS zero trust assessments, tailored to the DoD or CISA frameworks. Work with a dedicated zero trust specialist to complete an assessment of your current environment. These assessments help you identify your agency’s current zero trust maturity level, pinpoint gaps, and develop a customized roadmap aligned with your specific requirements and budgets. You can reassess your environment at any time to track progress over time.

Figure 1: Example of DoD phase maturity by pillar

Figure 1: Example of DoD phase maturity by pillar

Figure 2: Example of DoD phase activities by maturity level

Figure 2: Example of DoD phase activities by maturity level

Conclusion

The AWS Zero Trust Accelerator for Government (ZTAG) represents the commitment made by AWS to support US federal agencies in their transition to zero trust architectures. By combining the AWS Cloud infrastructure with industry-leading security solutions, ZTAG provides a government-centric and flexible approach to achieving a robust cybersecurity posture while maintaining operational agility.

Government agencies can use ZTAG to accelerate their zero trust adoption, enhance their overall security posture, and align with critical compliance requirements. Contact your AWS account team to learn more about how AWS can support your agency’s zero trust journey.

If you have feedback about this post, submit comments in the Comments section below.

Derek Doerr

Derek Doerr

Derek is a senior technology leader and Zero Trust Single-Threaded Leader for AWS US Federal, specializing in security strategy and cloud governance. With over 30 years of experience across private and public sectors, he drives strategic initiatives and maintains security culture. Outside of work, he enjoys spending time with family, cooking, scuba diving, and traveling.

Intelligent coding at your fingertips: Introducing an agentic coding experience in your IDE

Post Syndicated from Brian Beach original https://aws.amazon.com/blogs/devops/amazon-q-developer-agentic-coding-experience/

Back in March, I wrote about the new agentic coding experience within the Amazon Q Developer CLI. Recently, Amazon Q Developer announced that it has added a similar experience to the integrated development environement (IDE). Agentic coding in the IDE allows you to work with Amazon Q Developer to read and write files locally, run bash commands, build code, and more in near real-time through natural language conversations. The new experience redefines how you write, modify, and maintain code by leveraging natural language understanding to seamlessly execute complex workflows. The new agentic coding experience is now available in VS Code with support in other IDEs coming soon.

Background

Before I explain the new agentic coding experience, let’s take a minute to review the existing chat capabilities within the Amazon Q Developer IDE. As the name implies, the traditional chat allows me to have a conversation with Q Developer. This is a great option when I’m learning and planning. It provides a natural back-and-forth dialogue. Personally, I like the traditional chat during the planning phase of the Software Development Lifecycle (SDLC). I can chat with Q Developer to discuss my architecture and the various tradeoffs of different designs before I start working.

However, once I move into the build phase of the SDLC, I prefer the new agentic coding experience. In this new experience, Q Developer can do so much more than just have a conversation. It can directly interact with the development environment, reading and writing files, using various development tools, and even querying AWS resources. This allows for a far more dynamic, hands-on coding workflow compared to the traditional chat interface.

Rather than just discussing requirements, the agentic agent can take direct action to implement them. It can scaffold new projects, update existing code, and provide step-by-step summaries of its progress – all through a seamless, conversational interface right within the IDE. The great news is that I now have both options available to me. I can simply toggle between a traditional chat in the planning phase, and the new agentic coding in the build phase.

Walkthrough

Let’s walk through a simple example using the AWS Cloud Development Kit (CDK). I love CDK, and I use it all the time in my role. However, let’s assume that I don’t have a lot of experience, and want to learn more about CDK before I start using it. Since I just want to learn, I’ll start in the traditional chat experience, and ask Q Developer “How do I create an new CDK app?” As you can see in the following image, Q Developer starts to teach me about CDK. Along with the instructions, Q provides commands that I could copy and paste into my shell to get started.

A screenshot of an Amazon Q Developer chat interface showing instructions for creating a new AWS CDK app. The interface displays a dark theme with a conversation about CDK app creation. The response includes step-by-step instructions: installing the AWS CDK toolkit via npm, creating a new directory for the CDK project, and beginning to explain initialization commands. Command examples are shown in code blocks with copy buttons. The bottom of the screen shows an input field and a notice about Amazon Q's AI capabilities.

While this is a great, I am already familiar with CDK. I don’t need to learn how to create a new application. I am ready to start building! Therefore, I will toggle from traditional chat to agentic coding by clicking on the angle bracket pair in the bottom left corner of the chat window. Then, I will ask Q Developer to “Create a new CDK app in this folder using TypeScript.” First, notice that I am not asking a question like I did previously, but I am giving a command. In the following image, you can see that Q Developer is acting on my command rather that teaching me what to do.

A screenshot of an Amazon Q Developer chat interface with a dark theme. The image shows a conversation about creating a new AWS CDK app using TypeScript. The assistant provides instructions to initialize a CDK project in the current directory. A command prompt is displayed with the command "npx aws-cdk init app --language typescript" to create a new CDK TypeScript application. The interface includes "Reject" and "Run" options for the command. At the bottom, there's an input field for asking questions and a note about Amazon Q's use of generative AI.

This is the power of the new agentic coding. It is not simply teaching me how to create a CDK app. Amazon Q Developer is creating the app for me. There are a few important things that I want to call out here. First, Amazon Q Developer can use tools when it is running agentic coding mode. In this example, Q is using a series of shell commands — mkdir, cd, npx, npm, etc. — to create the CDK app. I will discuss other tools later in this post. Second, Q Developer is asking my permission before it runs these commands. This allows me to retain control over the development process. I’ll click the Run button and allow Q to create the new application resulting in the following project structure.

A screenshot of a directory view showing the structure of a TypeScript-based AWS CDK project. The project root folder "IDE-BLOG-POST" displays a typical CDK project structure, containing four main directories (bin, lib, node_modules, and test) along with several configuration and documentation files: .gitignore, .npmignore, cdk.json, jest.config.js, package-lock.json, package.json, README.md, and tsconfig.json. The interface uses a dark theme with distinctive icons indicating different file types and folder structures.

It’s easy to overlook the power of allowing Q Developer to use tools. By using shell commands, it was able to generate the project using the latest template, and install dependencies for me. Running shell commands is just one of many changes with the agentic coding experience. Next, let’s look at how code generation works in agentic coding.

Code Generation

Amazon Q Developer has been generating code since it first launched in June of 2022. Since then, Amazon Q Developer has evolved, adding new features over time. Code generation began with inline suggestions, followed by chat, and the agent for software development. The new agentic coding, reinvents the code generation experience again. In the following example, I am going to add a Lambda function to the CDK stack that Q Developer created earlier. I ask Q Developer to “Add a new Lambda function that is triggered from the arrival of a file in an existing S3 bucket.”

A screenshot of an Amazon Q Developer chat interface showing instructions for adding an S3-triggered Lambda function to an existing CDK stack. The interface displays several steps being executed: modifying the stack file (ide-blog-post-stack.ts with +41/-6 changes), creating a lambda directory using the "mkdir -p lambda" command (marked as completed), creating a Lambda function in index.js (+25/-0 changes), and updating the README.md file (+26/-4 changes). Each modification shows an "Undo" option, and there's an "Undo all changes" button at the bottom. The interface features a dark theme and includes the standard input field and AI disclosure notice at the bottom.

Multiple important things happened in this example that I want to explain. First, notice that Q Developer edited the CDK Stack to add the new AWS Lambda function. Second, Q Developer used a shell command to create a new folder. Third, Q created a new file for the Lambda function. Forth, it updated the README file. Q took all four of these actions in response to a single prompt. In addition, note that Q Developer is providing a diff for each change, making it easy for me to review the changes. You can see an example of the changes it make to the README.md in the following image. Finally, note that I can undo any of the changes that Q Developer made along the way.

A screenshot of a README.md file in a code editor with a dark theme. The file shows both removed content (in red) and new content (in green). The removed content is the default CDK TypeScript project introduction, while the new content describes an S3-triggered Lambda function CDK project. The new documentation includes an architecture section detailing the Lambda function, S3 bucket, and event notification components, followed by deployment instructions that include steps for building the project with 'npm run build' and deploying the stack with CDK using parameters for an existing bucket name.

This is a big improvement over the traditional chat experience. Now let’s look at how Q Developer can describe my AWS resources.

Describing AWS resources

Remember that I am building an application that is triggered by the arrival of a file in an existing Amazon Simple Storage Service (Amazon S3) bucket. In the prior example, you can see that I need to pass the name of the bucket in the ExistingBucketName parameter when deploying the stack.

Let’s assume that I have forgotten the name of the bucket I want to use. The new agentic coding experience can help me with this too. In the following example, I ask Q to “List my S3 buckets in the ca-central-1 region?” Once again, Q Developer asks for permission to use the shell. After I accept, Q Developer uses the AWS CLI and lists the buckets I have available in Canada (ca-central-1).

A screenshot of Amazon Q Developer displaying an AWS CLI command and its output showing S3 bucket listing for the ca-central-1 region. The command uses aws s3api list-buckets with jq filtering to show only buckets in the Canada Central region. The output displays one bucket named "blog-post-demo-bucket" with explanatory text about using it with Lambda functions and CDK stack deployment.

With the name of the bucket, I am ready to deploy my stack. Of course, there still more work to do, but I’ll leave that for another post.

Conclusion

The new agentic coding experience within the Amazon Q Developer IDE represents a significant step forward in integrating powerful AI-driven capabilities directly into the developer’s workflow. By enabling the coding agent to read, write, and execute code locally, access tools, and interact with AWS resources, Q Developer promises to dramatically streamline and enhance the coding process. You can visit the Amazon Q Developer User Guide to install the IDE and start leveraging the new agent chat for free. Give it a try and let me know what you think!

AWS Weekly Roundup: Amazon Nova Premier, Amazon Q Developer, Amazon Q CLI, Amazon CloudFront, AWS Outposts, and more (May 5, 2025)

Post Syndicated from Donnie Prakoso original https://aws.amazon.com/blogs/aws/aws-weekly-roundup-amazon-nova-premier-amazon-q-developer-amazon-q-cli-amazon-cloudfront-aws-outposts-and-more-may-5-2025/

Last week I went to Thailand to attend the AWS Summit Bangkok. It was an energizing and exciting event. We hosted the Developer Lounge, where developers can meet, discuss ideas, enjoy lightning talks, win SWAGs at AWS Builder ID Prize Wheel, take a challenge at Amazon Q Developer Coding Challenge, or learn Generative AI at Learn Amazon Bedrock booth.

Here’s a quick look:

Thank you to AWS Heroes, AWS Community Builders, AWS User Group leaders and developers for your collaboration.

Coming up next in ASEAN is AWS Summit Singapore—make sure you don’t miss it by registering now.

Last Week’s Launches
Here are some launches last week that caught my attention:

  • Amazon Nova Premier Now Generally Available — Amazon Nova Premier, our most capable model for complex tasks and teacher for model distillation, is now generally available in Amazon Bedrock. It excels at complex tasks requiring deep context understanding and multistep planning, while processing text, images, and videos with a 1M token context length. With Nova Premier and Amazon Bedrock Model Distillation, you can create highly capable, cost-effective, and low-latency versions of Nova Pro, Lite, and Micro, for your specific needs.

  • Amazon Q Developer elevates the IDE experience with new agentic coding experience — This new interactive, agentic coding experience for Visual Studio Code allows Q Developer to intelligently take actions on behalf of the developer. Amazon Q Developer introduces an interactive coding experience in Visual Studio Code, offering real-time collaboration for coding, documentation, and testing. It provides transparent reasoning, and supports automated or step-by-step changes in multiple languages.

  • New Foundation Models in Amazon Bedrock — Amazon Bedrock expands its model offerings with two significant additions:
    • Writer’s Palmyra X5 and X4 models feature extensive context windows (1M and 128K tokens respectively) and excel in complex reasoning for enterprise applications. They support multistep tool-calling and adaptive thinking with high reliability standards.
    • Meta’s Llama 4 Scout 17B and Maverick 17B models offer natively multimodal capabilities using mixture-of-experts architecture for enhanced reasoning and image understanding. They support multiple languages and extended context processing, with simplified integration through the Bedrock Converse API.
  • Second-Generation AWS Outposts Racks Released AWS announces the general availability of second-generation Outposts racks with significant enhancements including the latest x86 EC2 instances, simplified networking, and accelerated networking options. These improvements deliver doubled vCPU, memory, and network bandwidth, 40% better performance, and support for ultra-low latency workloads, making them ideal for demanding on-premises deployments.

  • Amazon CloudFront SaaS Manager Launches — Amazon CloudFront SaaS Manager helps SaaS providers and web hosting platforms efficiently manage content delivery across multiple customer domains. The service dramatically reduces operational complexity while providing high-performance content delivery and enterprise-grade security for every customer domain.

  • Amazon Aurora Now Supports PostgreSQL 17 — Amazon Aurora now supports PostgreSQL 17.4, offering community improvements and Aurora-specific enhancements like optimized memory management and faster failovers. The release includes new features for Babelfish, security fixes, and updated extensions, available in all AWS Regions.
  • CloudWatch Introduces Tiered Pricing for Lambda Logs — Amazon CloudWatch launches tiered pricing for AWS Lambda logs and new delivery destinations. Pricing in US East starts at $0.50/GB for CloudWatch and $0.25/GB for S3 and Firehose, both tiering down to $0.05/GB. This update enhances flexibility in log management across all supporting Regions.
  • RDS for MySQL Updates Minor VersionsAmazon RDS for MySQL now supports minor versions 8.0.42 and 8.4.5, delivering security fixes, bug fixes, and performance improvements. Users can upgrade automatically during maintenance windows or use Blue/Green deployments for safer updates.
  • Amazon Bedrock Model Distillation Generally AvailableAmazon Bedrock Model Distillation is now generally available, supporting new models like Amazon Nova and Claude 3.5. It enables smaller models to accurately predict function calling for Agents, delivering up to 500% faster responses and 75% lower costs with minimal accuracy loss for RAG use cases. The service includes automated workflows for data synthesis and student model training.
  • AI Search Flow Builder for Amazon OpenSearch Service Amazon OpenSearch Service now offers an AI search flow builder for OpenSearch 2.19+ domains. This low-code designer enables creation of sophisticated AI-enhanced search flows using AWS and third-party services, supporting use cases like RAG, query rewriting, and semantic encoding.

From Community.AWS
Here’s my personal favorites posts from community.aws:

Upcoming AWS events
Check your calendars and sign up for these upcoming AWS events:

  • AWS Summit — Join free online and in-person events that bring the cloud computing community together to connect, collaborate, and learn about AWS. Register in your nearest city: Poland (6 May), Bengaluru (May 7 – 8), Hong Kong (May 8), Seoul (May 14-15), Singapore (May 29), and Sydney (June 4–5).
  • AWS re:Inforce – Mark your calendars for AWS re:Inforce (June 16–18) in Philadelphia, PA. AWS re:Inforce is a learning conference focused on AWS security solutions, cloud security, compliance, and identity. You can subscribe for event updates now!
  • AWS Partners Events – You’ll find a variety of AWS Partner events that will inspire and educate you, whether you are just getting started on your cloud journey or you are looking to solve new business challenges.
  • AWS Community Days – Join community-led conferences that feature technical discussions, workshops, and hands-on labs led by expert AWS users and industry leaders from around the world: Yerevan, Armenia (May 24), Zurich, Switzerland (May 25), and Bengaluru, India (May 25).

You can browse all upcoming in-person and virtual events.

That’s all for this week. Check back next Monday for another Weekly Roundup!


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Amazon Q Developer in GitHub (in preview) accelerates code generation

Post Syndicated from Matheus Guimaraes original https://aws.amazon.com/blogs/aws/amazon-q-developer-in-github-now-in-preview-with-code-generation-review-and-legacy-transformation-capabilities/

Starting today, you can now use Amazon Q Developer in GitHub in preview! This is fantastic news for the millions of developers who use GitHub on a daily basis, whether at work or for personal projects. They can now use Amazon Q Developer for feature development, code reviews, and Java code migration directly within the GitHub interface.

To demonstrate, I’m going to use Amazon Q Developer to help me create an application from zero called StoryBook Teller. I want this to be an ASP.Core website using .NET 9 that takes three images from the user and uses Amazon Bedrock with Anthropic’s Claude to generate a story based on them.

Let me show you how this works.

Installation

The first thing you need to do is install the Amazon Q Developer application in GitHub, and you can begin using it immediately without connecting to an AWS account.

You’ll then be presented with a choice to add it to all your repositories or select specific ones. In this case, I want to add it to my storybook-teller-demo repo, so I choose Only selected repositories and type in the name to find it.

This is all you need to do to make the Amazon Q Developer app ready to use inside your selected repos. You can verify that the app is installed by navigating to your GitHub account Settings and the app should be listed in the Applications page.

You can choose Configure to view permissions and add Amazon Q Developer to repositories or remove it at any time.

Now let’s use Amazon Q Developer to help us build our application.

Feature development
When Amazon Q Developer is installed into a repository, you can assign GitHub issues to the Amazon Q development agent to develop features for you. It will then generate code using the whole codebase in your repository as context as well as the issue’s description. This is why it’s important to list your requirements as accurately and clearly as possible in your GitHub issues, the same way that you should always strive for anyway.

I have created five issues in my StoryBook Teller repository that cover all my requirements for this app, from creating a skeleton .NET 9 project to implementing frontend and backend.

Let’s use Amazon Q Developer to develop the application from scratch and help us implement all these features!

To begin with, I want Amazon Q Developer to help me create the .NET project. To do this, I open the first issue, and in the Labels section, I find and select Amazon Q development agent.

That’s all there is to it! The issue is now assigned to Amazon Q Developer. After the label is added, the Amazon Q development agent automatically starts working behind the scenes providing progress updates through the comments, starting with one saying, I'm working on it.

As you might expect, the amount of time it takes will depend on the complexity of the feature. When it’s done, it will automatically create a pull request with all the changes.

The next thing I want to do is make sure that the generated code works, so I’m going to download the code changes and run the app locally on my computer.

I go to my terminal and type git fetch origin pull/6/head:pr-6 to get the code for the pull request it created. I double-check the contents and I can see that I do indeed have an ASP.Core project generated using .NET 9, as I expected.

I then run dotnet run and open the app with the URL given in the output.

Brilliant, it works! Amazon Q Developer took care of implementing this one exactly as I wanted based on the requirements I provided in the GitHub issue. Now that I have tested that the app works, I want to review the code itself before I accept the changes.

Code review
I go back to GitHub and open the pull request. I immediately notice that Amazon Q Developer has performed some automatic checks on the generated code.

This is great! It has already done quite a bit of the work for me. However, I want to review it before I merge the pull request. To do that, I navigate to the Files changed tab.

I review the code, and I like what I see! However, looking at the contents of .gitignore, I notice something that I want to change. I can see that Amazon Q Developer made good assumptions and added exclusion rules for Visual Studio (VS) Code files. However, JetBrains Rider is my favorite integrated development environment (IDE) for .NET development, so I want to add rules for it, too.

You can ask Amazon Q Developer to reiterate and make changes by using the normal code review flow in the GitHub interface. In this case, I add a comment to the .gitignore code saying, add patterns to ignore Rider IDE files. I then choose Start a review, which will queue the change in the review.

I select Finish your review and Request changes.

Soon after I submit the review, I’m redirected to the Conversation tab. Amazon Q Developer starts working on it, resuming the same feedback loop and encouraging me to continue with the review process until I’m satisfied.

Every time Q Developer makes changes, it will run the automated checks on the generated code. In this case, the code was somewhat straightforward, so it was expected that the automatic code review wouldn’t raise any issues. But what happens if we have more complex code?

Let’s take another example and use Amazon Q Developer to implement the feature for enabling image uploads on the website. I use the same flow I described in the previous section. However, I notice that the automated checks on the pull request flagged a warning this time, stating that the API generated to support image uploads on the backend is missing authorization checks effectively allowing direct public access. It explains the security risk in detail and provides useful links.

It then automatically generates a suggested code fix.

When it’s done, you can review the code and choose to Commit changes if you’re happy with the changes.

After fixing this and testing it, I’m happy with the code for this issue and move on applying the same process to other ones. I assign the Amazon Q development agent to each one of my remaining issues, wait for it to generate the code, and go through the iterative review process asking it to fix any issues for me along the way. I then test my application at the end of that software cycle and am very pleased to see that Amazon Q Developer managed to handle all issues, from project setup, to boilerplate code, to more complex backend and frontend. A true full-stack developer!

I did notice some things that I wanted to change along the way. For example, it defaulted to using the Invoke API to send the uploaded images to Amazon Bedrock instead of the Converse API. However, because I didn’t state this in my requirements, it had no way of knowing. This highlights the importance of being as precise as possible in your issue’s titles and descriptions to give Q Developer the necessary context and make the development process as efficient as possible.

Having said that, it’s still straightforward to review the generated code on the pull requests, add comments, and let the Amazon Q Developer agent keep working on changes until you’re happy with the final result. Alternatively, you can accept the changes in the pull request and create separate issues that you can assign to Q Developer later when you’re ready to develop them.

Code transformation
You can also transform legacy Java codebases to modern versions with Q Developer. Currently, it can update applications from Java 8 or Java 11 to Java 17, with more options coming in future releases.

The process is very similar to the one I demonstrated earlier in this post, except for a few things.

First, you need to create an issue within a GitHub repository containing a Java 8 or Java 11 application. The title and description don’t really matter in this case. It might even be a short title such as “Migration,” leaving the description empty. Then, on Labels, you assign the Amazon Q transform agent label to the issue.

Much like before, Amazon Q Developer will start working immediately behind the scenes before generating the code on a pull request that you can review. This time, however, it’s the Amazon Q transform agent doing the work which is specialized in code migration and will take all the necessary steps to analyze and migrate the code from Java 8 to Java 17.

Notice that it also needs a workflow to be created, as per the documentation. If you don’t have it enabled yet, it will display clear instructions to help you get everything set up before trying again.

As expected, the amount of time needed to perform a migration depends on the size and complexity of your application.

Conclusion
Using Amazon Q Developer in GitHub is like having a full-stack developer that you can collaborate with to develop new features, accelerate the code review process, and rely on to enhance the security posture and quality of your code. You can also use it to automate migration from Java 8 and 11 applications to Java 17 making it much easier to get started on that migration project that you might have been postponing for a while. Best of all, you can do all this from the comfort of your own GitHub environment.

Now available
You can now start using Amazon Q Developer today for free in GitHub, no AWS account setup needed.

Amazon Q Developer in GitHub is currently in preview.

Matheus Guimaraes | codingmatheus


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Amazon Q Developer elevates the IDE experience with new agentic coding experience

Post Syndicated from Elizabeth Fuentes original https://aws.amazon.com/blogs/aws/amazon-q-developer-elevates-the-ide-experience-with-new-agentic-coding-experience/

Today, Amazon Q Developer introduces a new, interactive, agentic coding experience that is now available in the integrated development environments (IDE) for Visual Studio Code. This experience brings interactive coding capabilities, building upon existing prompt-based features. You now have a natural, real-time collaborative partner working alongside you while writing code, creating documentation, running tests, and reviewing changes.

Amazon Q Developer transforms how you write and maintain code by providing transparent reasoning for its suggestions and giving you the choice between automated modifications or step-by-step confirmation of changes. As a daily user of Amazon Q Developer command line interface (CLI) agent, I’ve experienced firsthand how Amazon Q Developer chat interface makes software development a more efficient and intuitive process. Having an AI-powered assistant only a q chat away in CLI has streamlined my daily development workflow, enhancing the coding process.

The new agentic coding experience in Amazon Q Developer in the IDE seamlessly interacts with your local development environment. You can read and write files directly, execute bash commands, and engage in natural conversations about your code. Amazon Q Developer comprehends your codebase context and helps complete complex tasks through natural dialog, maintaining your workflow momentum while increasing development speed.

Let’s see it in action
To begin using Amazon Q Developer for the first time, follow the steps in the Getting Started with Amazon Q Developer guide to access Amazon Q Developer. When using Amazon Q Developer, you can choose between Amazon Q Developer Pro, a paid subscription service, or Amazon Q Developer Free tier with AWS Builder ID user authentication.

For existing users, update to the new version. Refer to Using Amazon Q Developer in the IDE for activation instructions.

To start, I select the Amazon Q icon in my IDE to open the chat interface. For this demonstration, I’ll create a web application that transforms Jupiter notebooks from the Amazon Nova sample repository into interactive applications.

I send the following prompt: In a new folder, create a web application for video and image generation that uses the notebooks from multimodal-generation/workshop-sample as examples to create the applications. Adapt the code in the notebooks to interact with models. Use existing model IDs

Amazon Q Developer then examines the files: the README file, notebooks, notes, and everything that is in the folder where the conversation is positioned. In our case it’s at the root of the repository.

After completing the repository analysis, Amazon Q Developer initiates the application creation process. Following the prompt requirements, it requests permission to execute the bash command for creating necessary folders and files.

With the folder structure in place, Amazon Q Developer proceeds to build the complete web application.

In a few minutes, the application is complete. Amazon Q Developer provides the application structure and deployment instructions, which can be converted into a README file upon request in the chat.

During my initial attempt to run the application, I encountered an error. I described it in Spanish using Amazon Q chat.

Amazon Q Developer responded in Spanish and gave me the solutions and code modifications in Spanish! I loved it!

After implementing the suggested fixes, the application ran successfully. Now I can create, modify, and analyze images and videos using Amazon Nova through this newly created interface.

The preceding images showcase my application’s output capabilities. Because I asked to modify the video generation code in Spanish, it gave me the message in Spanish.

Things to know
Chatting in natural languages – Amazon Q Developer IDE supports many languages, including English, Mandarin, French, German, Italian, Japanese, Spanish, Korean, Hindi, and Portuguese. For detailed information, visit the Amazon Q Developer User Guide page.

Collaboration and understanding – The system examines your repository structure, files, and documentation while giving you the flexibility to interact seamlessly through natural dialog with your local development environment. This deep comprehension allows for more accurate and contextual assistance during development tasks.

Control and transparency – Amazon Q Developer provides continuous status updates as it works through tasks and lets you choose between automated code modifications or step-by-step review, giving you complete control over the development process.

Availability – Amazon Q Developer interactive, agentic coding experience is now available in the IDE for Visual Studio Code.

Pricing – Amazon Q Developer agentic chat is available in the IDE at no additional cost to both Amazon Q Developer Pro Tier and Amazon Q Developer Free tier users. For detailed pricing information, visit the Amazon Q Developer pricing page.

To learn more about getting started visit the Amazon Q Developer product web page.

— Eli


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AWS Lambda introduces tiered pricing for Amazon CloudWatch logs and additional logging destinations

Post Syndicated from Shridhar Pandey original https://aws.amazon.com/blogs/compute/aws-lambda-introduces-tiered-pricing-for-amazon-cloudwatch-logs-and-additional-logging-destinations/

Effective logging is an important part of an observability strategy when building serverless applications using AWS Lambda.

Lambda automatically captures and sends logs to Amazon CloudWatch Logs. This allows you to focus on building application logic rather than setting up logging infrastructure and allows operators to troubleshoot failures and performance issues more easily.

On May 1st, 2025, AWS announced changes to Lambda logging, which can reduce Lambda CloudWatch logging costs and make it easier and more cost-effective to use a wider range of monitoring tools. Lambda logs are now available at volume-based tiered pricing when using CloudWatch Logs Standard and Infrequent Access log classes. When generating Lambda logs at scale, you can expect an immediate cost reduction under this new pricing model. Lambda also now supports Amazon S3 and Amazon Data Firehose as additional destinations for Lambda logs, in addition to CloudWatch Logs. Lambda logs sent to S3 and Firehose are also available at volume-based tiered pricing.

This blog post covers some recent Lambda logging enhancements and describes how this change delivers a simpler, more cost-effective logging experience for Lambda.

Overview

Logging provides developers and operators with valuable data for debugging and troubleshooting application behavior, performance issues, and potential failures. It becomes even more important for serverless applications built using Lambda because of the ephemeral and stateless nature of the Lambda execution environment. Lambda’s built-in integration with CloudWatch Logs ensures that logs for every function invocation are readily available for analysis. The captured log data includes application logs generated by your Lambda function code and system logs generated by the Lambda service while running your function code. CloudWatch Logs allows you to search, filter, and analyze log data to troubleshoot issues, track metrics, and set up alerts.

Logging requirements evolve as serverless applications grow in complexity and scale, sometimes spanning hundreds or thousands of Lambda functions which generate substantial log volumes. Organizations need sophisticated logging solutions that can handle this scale while remaining cost-effective. Some scenarios—such as monitoring critical business transactions—demand real-time log analysis, while others focus on after-the-fact forensic analysis. Debug logs from development and staging environments often need high granularity, whereas you may want lower verbosity in production logs to improve the signal-to-noise ratio.

Recent Lambda logging enhancements

In recent years, Lambda and CloudWatch Logs have expanded Lambda’s logging capabilities to meet the evolving needs of serverless applications. These capabilities provide deeper insights, greater control, and more cost-effective solutions to capture, process, and consume logs to enhancing the serverless observability experience. Lambda advanced logging controls gives developers control over log generation and content. These controls allow you to capture Lambda logs in JSON structured format. You don’t have to use logging libraries and customize log levels (INFO, DEBUG, WARN, ERROR) separately for application and system logs. This helps reduce logging costs by ensuring only necessary logs are generated while maintaining appropriate visibility across different environments. For example, you can set verbose DEBUG level logging in development environments while limiting production logging to ERROR level to improve the signal-to-noise ratio and control costs.

The Infrequent Access log class for CloudWatch Logs introduced a cost-effective solution for logs that need retention but are accessed less frequently. Infrequent Access is 50% lower per GB ingestion price than the Standard log class This tailored set of capabilities allows you to reduce your logging costs while maintaining access to historical data for compliance, audit purposes, or forensic analysis.

CloudWatch Logs Live Tail is an interactive, real-time log streaming and analytics capability. Live Tail streamlines debugging and monitoring workflows; it allows you to observe log output as functions execute without navigating away from the Lambda console. This makes it easier to identify and diagnose issues during development and troubleshooting. Logs Live Tail is also available in Visual Code IDE.

Tiered pricing for Lambda logs in CloudWatch Logs

Starting today, Lambda logs sent to CloudWatch Logs are classed as Vended Logs, which are logs from specific AWS services that are available at volume tiered pricing. This replaces the previous flat rate model when using CloudWatch Logs Standard log class. For example, in the US East (N. Virginia) AWS Region, you were charged at $0.50 per GB when using Standard log class for your Lambda logs. Under the new pricing model, you are charged for sending your Lambda logs to CloudWatch Logs starting at $0.50 per GB for initial usage. As log volume increases, the price per GB automatically decreases through multiple tiers, reaching rates as low as $0.05 per GB in the lowest tier. This pricing change applies automatically to all Lambda logs sent to CloudWatch Logs, requiring no code or configuration changes from you.

Data Ingested CloudWatch Logs Standard CloudWatch Logs Infrequent Access
First 10 TB per month $0.50 per GB $0.25 per GB
Next 20 TB per month $0.25 per GB $0.15 per GB
Next 20 TB per month $0.10 per GB $0.075 per GB
Over 50 TB per month $0.05 per GB $0.05 per GB

Table 1: Tiered pricing for Lambda logs in CloudWatch Logs in US East (N. Virginia) Region

When generating Lambda logs at scale, you will see an immediate cost reduction under this new pricing model. For example, if you generate 60 TB of Lambda logs monthly in CloudWatch Logs, costs would decrease by 58% (from $30,000 to $12,500). The pricing tiers scale with your logging volume, ensuring that cost benefits increase as your application grows. This allows you to maintain comprehensive logging practices that previously may have been cost-prohibitive. Vended logs tiered pricing is applied on all vended logs ingested to CloudWatch and not tiered per service.

When ingesting other vended logs, such as Amazon Virtual Private Cloud flow logs and Amazon Route 53 resolver query logs, you will see larger discounts as the tiering is applied at a consolidated log ingestion volume.

New Lambda logging destinations: Amazon S3 and Amazon Data Firehose

Starting today, Lambda also supports Amazon S3 and Amazon Data Firehose as destinations for Lambda logs, in addition to CloudWatch Logs. When using S3 or Firehose as a destination, logging costs start at $0.25 per GB. The tiered pricing also applies, with rates reducing to as low as $0.05 per GB in the lowest tier. This tiering is also applied at a consolidated log ingestion volume.

Data Ingested Delivery Cost to Amazon S3 Delivery Cost to Amazon Data Firehose
First 10TB per month $0.25 per GB $0.25 per GB
Next 20TB per month $0.15 per GB $0.15 per GB
Next 20TB per month $0.075 per GB $0.075 per GB
Over 50TB per month $0.05 per GB $0.05 per GB

Table 2:Tiered pricing for Lambda logs delivery to Amazon S3 and Amazon Data Firehose in US East (N. Virginia) Region

Direct delivery of Lambda logs to S3 provides enhanced flexibility in log management. Support for Firehose streamlines Lambda log delivery to additional destinations such as Amazon OpenSearch Service, HTTP endpoints, and third-party observability providers. This matches the established log delivery pattern used with other AWS compute services such as Amazon Elastic Container Service (Amazon ECS) and Amazon Elastic Compute Cloud (Amazon EC2).

This new capability provides significant cost benefits and streamlines log delivery to additional logging destinations, making it easier to use a wider range of monitoring tools (including CloudWatch) when building serverless applications using Lambda.

New Lambda logging destinations in action

All new and existing Lambda functions have CloudWatch Logs as the default logging destination, with S3 and Firehose as alternative choices. When you select S3 or Firehose as your logging destination, Lambda sends logs to the selected destination via a new CloudWatch Logs Delivery log class. This log class enables efficient routing but doesn’t support CloudWatch Logs Standard log class features, such as Logs Insights and Live Tail.

To set up S3 or Firehose as the destination for your Lambda logs in the Lambda console:

  1. Navigate to the Lambda console, and select or create a function to set up an S3 or Firehose logging destination.
  2. In the Configuration tab, select Monitoring and operations tools on the left pane.
  3. Select Edit in the Logging configuration. This opens the Edit logging configuration page.

    Figure 1. Edit logging configuration in Lambda console

    Figure 1. Edit logging configuration in Lambda console

  4. In the Log destination section, select Amazon S3 or Amazon Data Firehose. Amazon CloudWatch Logs is the default selection.

    Figure 2. Select log destination in the Edit logging configuration page

    Figure 2. Select log destination in the Edit logging configuration page

  5. Under CloudWatch delivery log group, choose Create new log group or Existing log group.
  6. To create a new delivery log group to send logs to S3, enter a log group name and specify the destination S3 bucket. Provide an AWS Identity and Access Management (IAM) role for CloudWatch Logs to deliver logs to S3.
    Follow similar steps to send logs to a Firehose stream.

    Figure 3. Create new CloudWatch delivery log group for S3

    Figure 3. Create new CloudWatch delivery log group for S3

  7. To use an existing delivery log group, select one from the Delivery log group. The selected delivery log group must have a configured destination (S3 or Firehose) and match the destination you selected.

    Figure 4. Select existing CloudWatch delivery log group for Firehose

    Figure 4. Select existing CloudWatch delivery log group for Firehose

Advanced logging controls are also available for S3 and Firehose destinations. These controls include JSON structured format selection and log level filters for both application and system logs. This gives you enhanced log management controls for easier search, filter, and analysis. You can also use AWS Command Line Interface (AWS CLI) and infrastructure as code (IaC) tools such as AWS CloudFormation and AWS Cloud Development Kit (AWS CDK) to set up Lambda logs delivery to S3 and Firehose.

Best practices

To get the most out of the changes announced today, ensure that your logging strategy is closely aligned with the requirements of your workload. For example, consider sending critical production logs to CloudWatch Logs to take advantage of its advanced real-time analytics and alerting features. You now automatically benefit from volume-based discounts through tiered pricing in CloudWatch Logs for high-volume logging scenarios. For logs that need long-term retention for historical analysis, you can use S3’s storage classes to further reduce costs. When using your existing or third-party monitoring tools, direct integration through Firehose eliminates the need for custom forwarding solutions and associated costs.

Logging cost optimization extends beyond destination selection. Monitor log volumes regularly to understand the impact of pricing tiers. Implement appropriate retention policies to prevent unnecessary storage of old logs and log sampling for high-volume debug logs. Consider using different logging strategies across development, staging, and production environments to balance observability needs with cost efficiency.

Conclusion

Tiered pricing for Lambda logs in CloudWatch Logs and support for S3 and Firehose as additional logging destinations improves Lambda application observability. You can now manage logging costs at scale and expand Lambda monitoring solutions through cost-effective, easy-to-configure integrations. Whether you’re building new serverless applications or optimizing existing ones, these enhancements help you implement comprehensive logging strategies that scale cost-effectively with your workload.

The new features announced today are available in all commercial AWS Regions where Lambda and CloudWatch Logs are available. Support for configuring log delivery to S3 and Firehose in the Lambda console is available in US East (Ohio), US East (N. Virginia), US West (Oregon), and Europe (Ireland) Regions, with additional Regions coming soon. Review the Lambda documentation and CloudWatch Logs documentation to learn more about these features and how to use them. Review the CloudWatch pricing page to learn more about how these features are priced.

For more serverless learning resources, visit Serverless Land.

Amazon Nova Premier: Our most capable model for complex tasks and teacher for model distillation

Post Syndicated from Danilo Poccia original https://aws.amazon.com/blogs/aws/amazon-nova-premier-our-most-capable-model-for-complex-tasks-and-teacher-for-model-distillation/

Today we’re expanding the Amazon Nova family of foundation models announced at AWS re:Invent with the general availability of Amazon Nova Premier, our most capable model for complex tasks and teacher for model distillation.

Nova Premier joins the existing Amazon Nova understanding models available in Amazon Bedrock. Similar to Nova Lite and Pro, Premier can process input text, images, and videos (excluding audio). With its advanced capabilities, Nova Premier excels at complex tasks that require deep understanding of context, multistep planning, and precise execution across multiple tools and data sources. With a context length of one million tokens, Nova Premier can process extremely long documents or large code bases.

With Nova Premier and Amazon Bedrock Model Distillation, you can create highly capable, cost-effective, and low-latency versions of Nova Pro, Lite, and Micro, for your specific needs. For example, we used Nova Premier to distill Nova Pro for complex tool selection and API calling. The distilled Nova Pro had a 20% higher accuracy for API invocations compared to the base model and consistently matched the performance of the teacher, with the speed and cost benefits of Nova Pro.

Amazon Nova Premier benchmark evaluation
We evaluated Nova Premier on a broad range of benchmarks across text intelligence, visual intelligence, and agentic workflows. Nova Premier is the most capable model in the Nova family as measured across 17 benchmarks as shown in the table below.

Amazon Nova Premier Benchmark Evaluations

Nova Premier is also comparable to the best non-reasoning models in the industry and is equal or better on approximately half of these benchmarks when compared to other models in the same intelligence tier. Details of these evaluations are in the technical report.

Nova Premier is also the fastest and the most cost-effective model in Amazon Bedrock for its intelligence tier. For further details and comparison on pricing, please refer to the Bedrock pricing page.

Nova Premier can also be used as a teacher model for distillation, which means you can transfer its advanced capabilities for a specific use case into smaller, faster, and more efficient models like Nova Pro, Micro, and Lite for production deployments.

Using Amazon Nova Premier
To get started with Nova Premier, you first need to request access to the model in the Amazon Bedrock console. Navigate to Model access in the navigation pane, find Nova Premier, and toggle access.

Console screenshot.

Once you have access, you can use Nova Premier through the Amazon Bedrock Converse API providing in input a list of messages from the user and the assistant. Messages can include text, images, and videos. Here’s an example of a straightforward invocation using the AWS SDK for Python (Boto3):

import boto3
import json

AWS_REGION = "us-east-1"
MODEL_ID = "us.amazon.nova-premier-v1:0"

bedrock_runtime = boto3.client('bedrock-runtime', region_name=AWS_REGION)
messages = [
    {
        "role": "user",
        "content": [
            {
                "text": "Explain the differences between vector databases and traditional relational databases for AI applications."
            }
        ]
    }
]

response = bedrock_runtime.converse(
    modelId=MODEL_ID,
    messages=messages
)

response_text = response["output"]["message"]["content"][-1]["text"]

print(response_text)

This example shows how Nova Premier can provide detailed explanations for complex technical questions. But the real power of Premier comes with its ability to handle sophisticated workflows.

Multi-agent collaboration use case
Let’s explore a more complex scenario that showcases how Nova Premier works a multi-agent collaboration architecture for investment research.

The equity research process typically involves multiple stages: identifying relevant data sources for specific investments, retrieving required information from those sources, and synthesizing the data into actionable insights. This process becomes increasingly complex when dealing with different types of financial instruments like stock indices, individual equities, and currencies.

We can build this type of application using multi-agent collaboration in Amazon Bedrock, with Nova Premier powering the supervisor agent that orchestrates the entire workflow. The supervisor agent analyzes the initial query (for example, “What are the emerging trends in renewable energy investments?”), breaks it down into logical steps, determines which specialized subagents to engage, and synthesizes the final response.

For this scenario, I’ve created a system with the following components:

  1. A supervisor agent powered by Nova Premier
  2. Multiple specialized subagents powered by Nova Pro, each focusing on different financial data sources
  3. Tools that connect to financial databases, market analysis tools, and other relevant information sources

Multi-agent architectural diagram

When I submit a query about emerging trends in renewable energy investments, the supervisor agent powered by Nova Premier does the following:

  1. Analyzes the query to determine the underlying topics and sources to cover
  2. Selects the appropriate subagents specific to those topics and sources
  3. Each subagent retrieves their relevant economic indicators, technical analysis, and market sentiment data
  4. The supervisor agent synthesizes this information into a comprehensive report for review by a financial professional

Utilizing Nova Premier in a multi-agent collaboration architecture such as this streamlines the financial professional’s work and helps them formulate their investment analysis faster. The following video provides a visual description of this scenario.

The key advantage of using Nova Premier for the supervisor role is its accuracy in coordinating complex workflows, so that the right data sources are consulted in the optimal sequence and each subagent receives in input the correct information for their work, resulting in higher quality insights.

Multi-agent collaboration with model distillation
Although Nova Premier provides the highest level of accuracy of its family of models, you might want to optimize latency and cost in production environments. This is where the strength of Nova Premier as a teacher model for distillation becomes interesting. Using Amazon Bedrock Model Distillation, we can customize Nova Micro from the results of Nova Premier for this specific investment research use case.

Unlike traditional fine-tuning that requires human feedback and labeled examples, with model distillation you can generate high-quality training data by having a teacher model produce the desired outputs, streamlining the data acquisition process.

Amazon Bedrock Model Distillation diagram

The process to distill a model involves:

  1. Generating synthetic training data by capturing input and output from Nova Premier runs across multiple financial instruments
  2. Using this data as a reference to train a customized version of Nova Micro through custom fine-tuning tools
  3. Evaluating the difference in latency and performance of the customized Micro model
  4. Deploying the customized Micro model as the supervisor agent in production

With Amazon Bedrock, you can further streamline the process and use invocation logs for data preparation. To do that, you need to set the model invocation logging on and set up an Amazon Simple Storage Service (Amazon S3) bucket as the destination for the logs.

Customer voices
Some of our customers had early access to Nova Premier. This is what they shared with us:

“Amazon Nova Premier has been outstanding in its ability to execute interactive analysis workflows, while still being faster and nearly half the cost compared to other leading models in our tests,” said Curtis Allen, Senior Staff Engineer at Slack, a company bringing conversations, apps, and customers together in one place.

“Implementing new solutions built on top of Amazon Nova has helped us with our mission of democratizing finance for all,” said Dev Tagare, Head of AI and Data at Robinhood Markets, a company on a mission to democratize finance for all. “We’re particularly excited about the ability to explore new avenues like complex multi-agent collaborations that are not just highly performing but also cost effective and fast. The intelligence of Nova Premier and what it can transfer to the other models like Nova Micro, Nova Lite, and Nova Pro unlocks multi-agent collaboration at a performance, price, and speed that will make it accessible to everyday customers.”

“Accelerating real-world AI deployments—not just prototypes—requires the ability to build models that are specialized for the unique needs of real world applications,” said Henry Ehrenberg, co-founder of Snorkel AI, a technology company that empowers data scientists and developers to quickly turn data into accurate and adaptable AI applications. “We’re excited to see AWS pushing efficient model customization forward with Amazon Bedrock Model Distillation and Amazon Nova Premier. These new model capabilities have the potential to accelerate our enterprise customers in building production AI applications, including Q&A applications with multimodal data and more.”

Things to know

Nova Premier is available in Amazon Bedrock in the US East (N. Virginia), US East (Ohio), and US West (Oregon) AWS Regions today via cross-Region inference. With Amazon Bedrock, you only pay for what you use. For more information, visit Amazon Bedrock pricing.

Customers in the US can also access Amazon Nova models at https://nova.amazon.com, a website to easily explore our FMs.

Nova Premier is our best teacher for distilling custom variants of Nova Pro, Micro, and Lite, which means you can capture the capabilities offered by Premier in smaller, faster models for production deployment.

Nova Premier includes built-in safety controls to promote responsible AI use, with content moderation capabilities that help maintain appropriate outputs across a wide range of applications.

To get started with Nova Premier, visit the Amazon Bedrock console today. For more information, see the Amazon Nova User Guide and send feedback to AWS re:Post for Amazon Bedrock. Explore the generative AI section of our community.aws site to see how our Builder communities are using Amazon Bedrock in their solutions.

Danilo


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Announcing second-generation AWS Outposts racks with breakthrough performance and scalability on-premises

Post Syndicated from Micah Walter original https://aws.amazon.com/blogs/aws/announcing-second-generation-aws-outposts-racks-with-breakthrough-performance-and-scalability-on-premises/

Today we’re announcing the general availability of second-generation AWS Outposts racks, which marks the latest innovation from AWS for edge computing. This new generation includes support for the latest x86-powered Amazon Elastic Compute Cloud (Amazon EC2) instances, new simplified network scaling and configuration, and accelerated networking instances designed specifically for ultra-low latency and high-throughput workloads. These enhancements deliver greater performance for a broad range of on-premises workloads, such as core trading systems of financial services and telecom 5G Core workloads.

Customers like athenahealth, FanDuel, First Abu Dhabi Bank, Mercado Libre, Liberty Latin America, Riot Games, Vector Limited, and Wiwynn are already using Outposts racks for workloads that need to stay on-premises. The second-generation Outposts rack can provide low latency, local data processing, or data residency needs, such as game servers for multi-player online games, customer transaction data, medical records, industrial and manufacturing control systems, telecom Business Support Systems (BSS), and edge inference of a variety of machine learning (ML) models. Customers can now take advantage of the latest generation of processors and more advanced configurations of Outposts racks to support faster processing, higher memory capacity, and increased network bandwidth.

Latest generation EC2 instances

We’re excited to announce local support for the latest generation (7th generation) of x86-powered Amazon EC2 instances on AWS Outposts racks, starting with C7i compute-optimized instances, M7i general-purpose instances, and R7i memory-optimized instances. These new instances deliver twice the vCPU, memory, and network bandwidth while providing up to 40% better performance compared to C5, M5, and R5 instances on previous generation Outposts racks. They are powered by 4th Gen Intel Xeon Scalable processors and are ideal for a broad range of on-premises workloads requiring enhanced performance such as larger databases, more memory-intensive applications, advanced real-time big data analytics, high-performance video encoding and streaming, and CPU-based edge inference with more sophisticated ML models. Support for more latest generation EC2 instances, including GPU-enabled instances, is coming soon.

Simplified network scaling and configuration

We’ve completely reimagined networking in our latest Outposts generation, making it simpler and more scalable than ever. At the heart of this upgrade is our new Outposts network rack, which acts as a central hub for all your compute and storage traffic.

This new design brings three major benefits to the table. First, you can now scale your compute resources independently from your networking infrastructure, giving you more flexibility and cost efficiency as your workloads grow. Second, we’ve built in network resilience from the ground up, with the network rack automatically handling device failures to keep your systems running smoothly. Third, connecting to your on-premises environment and AWS Regions is now a breeze – you can configure everything from IP addresses to VLAN and BGP settings through straightforward APIs or our updated console interface.

Image of an AWS Outposts rack device

Specialized Amazon EC2 instances with accelerated networking

We’re introducing a new category of specialized Amazon EC2 instances on Outposts racks with accelerated networking. These instances are purpose built for the most latency-sensitive, compute-intensive, and throughput-intensive mission-critical workloads on-premises. To deliver the best possible performance, in addition to the Outpost logical network, these instances feature a secondary physical network with network accelerator cards connected to top-of-rack (TOR) switches.

First in this category are bmn-sf2e instances, designed for ultra-low latency with deterministic performance. The new instances run on Intel’s latest Sapphire Rapids processors (4th Gen Xeon Scalable), delivering 3.9 GHz sustained performance across all cores with generous memory allocation – 8GB of RAM for every CPU core. We’ve equipped bmn-sf2e instances with AMD Solarflare X2522 network cards that connect directly to top-of-rack switches.

For financial services customers, especially capital market firms, these instances offer deterministic networking through native Layer 2 (L2) multicast, precision time protocol (PTP), and equal cable lengths. This enables customers to meet regulatory requirements around fair trading and equal access while easily connecting to their existing trading infrastructure.

Instance Name vCPUs Memory (DDR5) Network Bandwidth NVMe SSD Storage Accelerated Network Cards Accelerated Bandwidth (Gbps)
bmn-sf2e.metal-16xl 64 512 GiB 25 Gbps 2 x 8 TB (16 TB) 2 100
bmn-sf2e.metal-32xl 128 1024 GiB 50 Gbps 4 x 8 TB (32 TB) 4 200

The second instance type, bmn-cx2, is optimized for high throughput and low latency. This instance features NVIDIA ConnectX-7 400G NICs physically connected to high-speed top-of-rack switches, delivering up to 800 Gbps bare metal network bandwidth operating at near line rate. With native Layer 2 (L2) multicast and hardware PTP support, this instance is ideal for high-throughput workloads like real-time market data distribution, risk analytics, and telecom 5G core network applications.

Instance Name vCPUs Memory (DDR5) Network Bandwidth NVMe SSD Storage Accelerated Network Cards Accelerated Bandwidth (Gbps)
bmn-cx2.metal-48xl 192 1536 GiB 50 Gbps 4 x 4 TB (16 TB) 2 800

Bottom line, the new generation of Outposts racks deliver enhanced performance, scalability, and resiliency for a broad range of on-premises workloads, even for mission-critical workloads with the most stringent latency and throughput requirements. You can make your selection and initiate your order from the AWS Management Console. The new instances maintain consistency with regional deployments by supporting the same APIs, AWS Management Console, automation, governance policies, and security controls in the cloud and on-premises, improving developer productivity and IT efficiency.

Things to know

At launch, second-generation Outposts racks can be shipped to US and Canada and be parented back to 6 AWS Regions including US East (N. Virginia and Ohio), US West (Oregon), EU West (London and France) and Asia Pacific (Singapore). Support for more countries and territories and AWS Regions is coming soon. At launch, second-generation Outposts racks locally support a subset of AWS services found in previous generation Outposts racks. Support for more EC2 instance types and more AWS services is coming soon.

To learn more, visit the AWS Outposts racks product page and user guide. You can also talk to an Outposts expert if you are ready to discuss your on-premises needs.

— Micah;


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AWS Lambda standardizes billing for INIT Phase

Post Syndicated from Shubham Gupta original https://aws.amazon.com/blogs/compute/aws-lambda-standardizes-billing-for-init-phase/

Effective August 1, 2025, AWS will standardize billing for the initialization (INIT) phase across all AWS Lambda function configurations. This change specifically affects on-demand invocations of Lambda functions packaged as ZIP files that use managed runtimes, for which the INIT phase duration was previously unbilled. This update standardizes billing of the INIT phase across all runtime types, deployment packages, and invocation modes. Most users will see minimal impact on their overall Lambda bill from this change, as the INIT phase typically occurs for a very small fraction of function invocations. In this post, we discuss the Lambda Function Lifecycle and upcoming changes to INIT phase billing. You will learn what happens in the INIT phase and when it occurs, how to monitor your INIT phase duration, and strategies to optimize this phase and minimize costs.

Understanding the Lambda function execution lifecycle

The Lambda function execution lifecycle consists of three distinct phases: INIT, INVOKE, and SHUTDOWN. The INIT phase is triggered during a “cold start” when Lambda creates a new execution environment for a function in response to an invocation. This is followed by the INVOKE phase where the request is processed, and finally, the SHUTDOWN phase where the execution environment is terminated. For a summary of the execution lifecycle, watch AWS Lambda execution environment lifecycle.

During the INIT phase, Lambda performs a series of preparatory steps within a maximum duration of 10 seconds. The service retrieves the function code from an internal Amazon S3 bucket, or from Amazon Elastic Container Registry (Amazon ECR) for functions using container packaging. Then, it configures an environment with the specified memory, runtime, and other settings. When the execution environment is prepared, Lambda executes four key tasks in sequence:

  1. Initiate any extensions configured (Extension INIT)
  2. Bootstrap the runtime (Runtime INIT)
  3. Execute the function’s static code (Function INIT)
  4. Run any before-checkpoint runtime hooks (applicable only for Lambda SnapStart)

Understanding the billing changes

Lambda charges are based on the number of requests and the duration it takes for the code to run. The duration is calculated from the moment the function code begins running until it completes or terminates, rounded up to the nearest millisecond. Duration cost depends on the amount of memory that you allocate to your function.
https://docs.aws.amazon.com/lambda/latest/dg/provisioned-concurrency.html
Previously, the INIT phase duration wasn’t included in the Billed Duration for functions using managed runtimes with ZIP archive packaging, as evidenced in Amazon CloudWatch logs:

REPORT RequestId: xxxxx   Duration: 250.06 ms  Billed Duration: 251 ms  Memory Size: 1024 MB
Max Memory Used: 350 MB   Init Duration: 100.77 ms

However, functions configured with custom runtimes, Provisioned Concurrency (PC), or OCI packaging already included the INIT phase duration in their Billed Duration. Effective August 1, 2025, INIT phase will be billed across all configuration types and the INIT phase duration will be included in the Billed Duration for on-demand invocations of functions using managed runtimes with ZIP archive packaging as well. After this change, the REPORT Request ID log line will show the following:

REPORT RequestId: xxxxx   Duration: 250.06 ms  Billed Duration: 351 ms  Memory Size: 1024 MB
Max Memory Used: 350 MB   Init Duration: 100.77 ms 

The further INIT phase duration charges will follow the standard on-demand duration pricing that is specific to each AWS Region, which can be found on the Lambda pricing page. For AWS Lambda@Edge functions, the INIT phase duration will be billed according to Lambda@Edge duration rates.

Finding the INIT phase duration and impact to Lambda billing

You can already monitor the time spent in the INIT phase of your function invocations using the “init_duration” CloudWatch metric. This metric is also reported as “Init Duration” in the “REPORT RequestId” log line within CloudWatch Logs. These tools offer valuable insights into the INIT time of Lambda functions, which will now be factored into billing calculations.

For a more comprehensive analysis, you can use the following CloudWatch Log Insights query to generate a detailed report estimating the previously unbilled duration of the INIT phase. The query helps you understand the proportion of the unbilled INIT phase time relative to your overall Lambda usage, enabling more accurate cost projections following this billing change.

filter @type = "REPORT" and @billedDuration < (@duration + @initDuration) 
| stats sum((@memorySize/1000000/1024) * (@billedDuration/1000)) as BilledGBs, 
sum((@memorySize/1000000/1024) * ((ceil(@duration + @initDuration) - @billedDuration)/1000)) as UnbilledInitGBs, 
(UnbilledInitGBs/ (UnbilledInitGBs+BilledGBs)) as Ratio

The CloudWatch Log Insights query provides three essential metrics:

  1. BilledGBs: Represents the total GB-s (gigabyte-seconds) currently being billed for the chosen log groups.
  2. UnbilledInitGBs: Shows the total GB-s consumed during INIT phase that was previously not included in billing.
  3. Ratio: Indicates the percentage of total GB-s attributed to previously unbilled INIT phase duration.

Using these existing monitoring capabilities allows you to proactively assess and optimize your Lambda function INIT times, potentially minimizing the impact of the new billing structure on your overall costs.

Understanding and optimizing Lambda INIT phase

The Lambda INIT phase is triggered in two specific scenarios: during the creation of a new execution environment and when a function scales up to meet demand. This INIT code runs only during these “cold starts” and is bypassed during subsequent invocations that use existing warm environments. After the INIT phase, Lambda runs the function handler code to process the invocation.

Following the handler execution, Lambda freezes the execution environment. To improve resource management and performance, the Lambda service retains the execution environment for a non-deterministic period of time. During this time, if another request arrives for the same function, then the service may reuse the environment. This second request typically finishes faster, because the execution environment already exists and it isn’t necessary to download the code and run the INIT code. This is called a “warm start.”

Developers can use the INIT phase to create, initialize, and configure objects expected to be reused across multiple invocations during function INIT instead of doing it in the handler. Initializing the dependencies/shared objects upfront reduces the latency of subsequent invocations. For example:

  • Download more libraries or dependencies
  • Establish client connections to other AWS services such as Amazon S3 or Amazon DynamoDB
  • Create database connections to be shared across invocations
  • Retrieve application parameters or secrets from Amazon Systems Manager Parameter Store or AWS Secrets Manager

When developing Lambda functions, it’s important to strategically decide what code runs during the INIT phase as opposed to the handler phase, because it affects both performance and costs.

Optimizing package/library size

The INIT phase includes creating an execution environment, downloading the function code and initializing it. Three main factors influence its performance:

  1. The size of the function package, in terms of imported libraries and dependencies, and Lambda layers.
  2. The amount of code and INIT work.
  3. The performance of libraries and other services in setting up connections and other resources.

Larger function packages increase code download times. You can decrease INIT phase duration by reducing package size, resulting in faster cold starts and lower INIT costs. Furthermore, optimizing loading of libraries can also significantly impact package size. For example, in Node.js functions, you should use specific path imports (for example import DynamoDB from "aws-sdk/clients/dynamodb") rather than wildcard imports (for example import {* as AWS} from "aws-sdk") to speed up the INIT phase. Tools such as esbuild can further optimize performance by minifying and bundling packages. For details, read Optimizing node.js dependencies in AWS Lambda.

Optimizing INIT phase execution and cost efficiency

The frequency of INIT phase executions (or cold starts) directly impacts both performance and cost efficiency. According to an analysis of production Lambda workloads, INITs (cold starts) typically occur in under 1% of invocations—meaning code in the INIT phase may execute just once per hundred invocations.

You can use the INIT phase to perform one-time operations that benefit subsequent invocations. Common optimization patterns include pre-calculating lookup tables or transforming static datasets. For example, downloading static data from Amazon S3 or DynamoDB during INIT, making it available for all subsequent function invocations without repeated downloads.

Lambda SnapStart

Lambda SnapStart provides an effective solution for reducing cold start latency and INIT phase costs. When it’s enabled, SnapStart creates a snapshot during the first function INIT and reuses it for subsequent cold starts, eliminating the need for repeated INIT phase executions. This approach is particularly valuable for functions with longer INIT times due to loading module dependencies/frameworks, initializing the runtime, or executing one-time INIT code. SnapStart is supported for Java, .NET, and Python runtimes. You can implement SnapStart through the Lambda console or AWS Command Line Interface (AWS CLI), making sure that your code adheres to the AWS serialization guidelines for snapshot restoration compatibility. Using SnapStart allows you to significantly improve function startup times and optimize costs across multiple popular programming languages.

Provisioned Concurrency

Provisioned Concurrency is a Lambda feature that pre-initializes execution environments before any invocations occur. This proactive approach effectively eliminates the performance impact of the INIT phase on individual function calls, because the INIT is completed in advance.

Although all functions using the Provisioned Concurrency benefit from reduced startup times as compared to on-demand execution, the impact is particularly pronounced for certain runtime environments. For example, C# and Java functions—which typically experience slower INIT but faster execution times as compared to Node.js or Python—can achieve significant performance gains through this feature. Implementing Provisioned Concurrency allows you to effectively manage both consistent traffic patterns and expected usage spikes, thereby minimizing cold start latency across your serverless applications. This optimization strategy is particularly valuable for functions with complex INIT requirements or those serving latency-sensitive workloads. From a cost optimization perspective, Provisioned Concurrency is most suitable for workloads with sustained usage patterns above 60% usage, because this typically provides better cost efficiency compared to on-demand execution.

Conclusion

Effective August 1, 2025, AWS is standardizing the INIT phase billing for AWS Lambda. AWS provides multiple ways for you to optimize both the performance and costs of your Lambda functions. Whether you’re using SnapStart, implementing Provisioned Concurrency, or optimizing INIT code, we recommend working closely with AWS support teams to identify the most suitable optimization approach for your specific workload requirements.

For more support and guidance, consider participating in AWS Cost Optimization workshops or consulting the Lambda documentation.

Extend the Amazon Q Developer CLI with Model Context Protocol (MCP) for Richer Context

Post Syndicated from Brian Beach original https://aws.amazon.com/blogs/devops/extend-the-amazon-q-developer-cli-with-mcp/

Earlier today, Amazon Q Developer announced Model Context Protocol (MCP) support in the command line interface (CLI). Developers can connect external data sources to Amazon Q Developer CLI with MCP support for more context-aware responses. By integrating MCP tools and prompts into Q Developer CLI, you get access to an expansive list of pre-built integrations or any MCP Servers that support stdio. This extra context helps Q Developer write more accurate code, understand your data structures, generate appropriate unit tests, create database documentation, and execute precise queries, all without needing to develop custom integration code. By extending Q Developer with MCP tools and prompts, developers can execute development tasks faster, streamlining the developer experience. At AWS, we’re committed to supporting popular open source protocols for agents like Model Context Protocol (MCP) proposed by Anthropic. We’ll continue to support this effort by extending this functionality within the Amazon Q Developer IDE plugins in the coming weeks.

Introduction

I’m always on the lookout for tools and technologies that can streamline my workflow and unlock new capabilities. That’s why I was excited about the recent addition of Model Context Protocol (MCP) support in the Amazon Q Developer command line interface (CLI). MCP is an open protocol that standardizes how applications can seamlessly integrate with LLMs, providing a common way to share context, access data sources, and enable powerful AI-driven functionality. You can read more about MCP in this introduction.

Q Developer has had the ability to use tools for a while. I previously discussed the ability to run CLI commands and describe AWS resources. With the Q Developer CLI’s support for MCP tools and prompts, I now have the ability to add additional tools. For example, while I have had the ability to describe my AWS resources, I also need to describe database schemas, message formats, etc. to build an application. Let’s see how I can configure MCP to provide this additional context.

In this post, I will configure an MCP server to provide Q Developer with my database schema for a simple Learning Management System (LMS) that I am working on. While Q Developer is great at writing SQL, it does not know the schema of my database. The table structure and relationships are stored in the database and are not part of the source code of my project. Therefore, I am going to use an MCP server that can query the database schema. Specifically, I am using the official PostgreSQL reference implementation to connect to my Amazon Relational Database Service (RDS). Let’s get started.

Before Model Context Protocol

Prior to the introduction of MCP support, the Q Developer CLI provided a set of native tools, including the ability to execute bash commands, interact with files and the file system, and even make calls to AWS services. However, when it came to querying a database, the CLI was limited in its capabilities.

For example, prior to configuring the MCP server, I asked Q Developer to “Write a query that lists the students and the number of credits each student is taking.” In the following image you can see that Q Developer could only provide a generic SQL query, as it lacked the specific knowledge of the database schema for my LMS.

Screenshot of Amazon Q Developer CLI showing a response to a query request. The response includes explanatory text acknowledging the lack of schema information, followed by a generic SQL query written in green text. The query joins students, student_courses, and courses tables to calculate total credit hours per student, demonstrating Q's limited ability without MCP configuration.

While this is a great start, I know that Q developer could do so much more if it knew the database schema.

Configuring Model Context Protocol

The introduction of MCP support in the Q Developer CLI allows me to easily configure MCP servers. I configure one or more MCP servers in a file called mcp.json. I can store the configuration in my home directory (e.g. ~/.aws/amazonq/mcp.json) and it is applied to all projects on my machine. Alternatively, I can store the configuration in the workspace root (e.g. .amazonq/mcp.json) so it is shared among project members. Here is an example of the configuration for the PostgreSQL MCP server.

{
  "mcpServers": {
    "postgres": {
      "command": "npx",
      "args": [
        "-y",
        "@modelcontextprotocol/server-postgres",
        "postgresql://USERNAME:PASSWORD@HOST:5432/DBNAME"
      ]
    }
  }
}

With the MCP server configured, let’s see how Amazon Q Developer enhances my experience.

After Model Context Protocol

First, I start a new Q Developer session and immediately see the benefits. In addition to the existing tools, Q Developer now has access to PostgreSQL as shown in the following image. This means I can easily explore the schema of my database, understand the structure of the tables, and even execute complex SQL queries, all without having to write any additional integration code.

Screenshot of Amazon Q Developer CLI displaying a list of available tools. The tools are categorized into file system tools, bash execution, AWS tools, PostgreSQL database tools, and issue reporting. The PostgreSQL category is highlighted, showing the integration of MCP for database access.

Let’s test the MCP server by asking Q Developer to “List the database tables.” As you can see in the following example, Q Developer now understands that I am asking about the PostgreSQL database, and uses the MCP server to list my three tables: students, courses, and enrollment.

Screenshot of Amazon Q Developer CLI showing a database table listing request and response. The response shows a tool request using list_objects command with JSON parameters, followed by execution status and a list of three tables in the public schema: courses, enrollment, and students.

Let’s go back to the example from earlier in this post. Now, when I ask Q Developer to “Write a query that lists the students and the number of credits each student is taking,” it no longer responds with a generic query. Instead, Q Developer first describes the relevant tables in my database, generates the appropriate SQL query, and then executes it, providing me with the desired results.

Screenshot of Amazon Q Developer CLI showing a complete SQL query workflow. The image displays a precise SQL query in green syntax highlighting, followed by a results table showing student credit information, and an explanation of how the query works through five numbered steps. This demonstrates Q's ability to generate, execute, and explain database queries with schema knowledge.

Of course, Q Developer can do a lot more than just write queries. Q Developer can use the MCP server to write Java code that accesses the database, create unit tests for the data layer, document the database, and much more. For example, I asked Q Developer to “Create an entity-relationship (ER) diagram using Mermaid syntax.” Q Developer was able to generate a visual representation of the database schema, helping me better understand the relationships between the various entities.

Entity-Relationship (ER) diagram generated by Amazon Q Developer. The diagram shows three tables: STUDENTS, COURSES, and ENROLLMENT. Each table is represented by a box containing column names and data types. The ENROLLMENT table links STUDENTS and COURSES with 'enrolls in' and 'has enrolled' relationships. Primary and foreign keys are indicated. This visualizes the database schema structure for the Learning Management System.

The integration of MCP into the Q Developer CLI has significantly streamlined my workflow by allowing me to add additional tools as needed.

Conclusion

The addition of MCP support in the Amazon Q Developer CLI provides a standardized way to share context and access data sources. In this post, I’ve demonstrated how I can use the Q Developer CLI’s MCP integration to quickly set up a connection to a PostgreSQL database, explore the schema, and generate complex SQL queries without having to write any additional integration code. Moving forward, I’m excited to see how you can leverage MCP to further enhance your development workflow. I encourage you to explore the MCP capabilities and the AWS MCP Servers repository on GitHub.