All posts by Channy Yun (윤석찬)

DeepSeek-V3.1 model now available in Amazon Bedrock

Post Syndicated from Channy Yun (윤석찬) original https://aws.amazon.com/blogs/aws/deepseek-v3-1-now-available-in-amazon-bedrock/

In March, Amazon Web Services (AWS) became the first cloud service provider to deliver DeepSeek-R1 in a serverless way by launching it as a fully managed, generally available model in Amazon Bedrock. Since then, customers have used DeepSeek-R1’s capabilities through Amazon Bedrock to build generative AI applications, benefiting from the Bedrock’s robust guardrails and comprehensive tooling for safe AI deployment.

Today, I am excited to announce DeepSeek-V3.1 is now available as a fully managed foundation model in Amazon Bedrock. DeepSeek-V3.1 is a hybrid open weight model that switches between thinking mode (chain-of-thought reasoning) for detailed step-by-step analysis and non-thinking mode (direct answers) for faster responses.

According to DeepSeek, the thinking mode of DeepSeek-V3.1 achieves comparable answer quality with better results, stronger multi-step reasoning for complex search tasks, and big gains in thinking efficiency compared with DeepSeek-R1-0528.

Benchmarks DeepSeek-V3.1 DeepSeek-R1-0528
Browsecomp 30.0 8.9
Browsecomp_zh 49.2 35.7
HLE 29.8 24.8
xbench-DeepSearch 71.2 55.0
Frames 83.7 82.0
SimpleQA 93.4 92.3
Seal0 42.6 29.7
SWE-bench Verified 66.0 44.6
SWE-bench Multilingual 54.5 30.5
Terminal-Bench 31.3 5.7
(c)
https://api-docs.deepseek.com/news/news250821

DeepSeek-V3.1 model performance in tool usage and agent tasks has significantly improved through post-training optimization compared to previous DeepSeek models. DeepSeek-V3.1 also supports over 100 languages with near-native proficiency, including significantly improved capability in low-resource languages lacking large monolingual or parallel corpora. You can build global applications to deliver enhanced accuracy and reduced hallucinations compared to previous DeepSeek models, while maintaining visibility into its decision-making process.

Here are your key use cases using this model:

  • Code generation – DeepSeek-V3.1 excels in coding tasks with improvements in software engineering benchmarks and code agent capabilities, making it ideal for automated code generation, debugging, and software engineering workflows. It performs well on coding benchmarks while delivering high-quality results efficiently.
  • Agentic AI tools – The model features enhanced tool calling through post-training optimization, making it strong in tool usage and agentic workflows. It supports structured tool calling, code agents, and search agents, positioning it as a solid choice for building autonomous AI systems.
  • Enterprise applications – DeepSeek models are integrated into various chat platforms and productivity tools, enhancing user interactions and supporting customer service workflows. The model’s multilingual capabilities and cultural sensitivity make it suitable for global enterprise applications.

As I mentioned in my previous post, when implementing publicly available models, give careful consideration to data privacy requirements when implementing in your production environments, check for bias in output, and monitor your results in terms of data security, responsible AI, and model evaluation.

You can access the enterprise-grade security features of Amazon Bedrock and implement safeguards customized to your application requirements and responsible AI policies with Amazon Bedrock Guardrails. You can also evaluate and compare models to identify the optimal model for your use cases by using Amazon Bedrock model evaluation tools.

Get started with the DeepSeek-V3.1 model in Amazon Bedrock
If you’re new to using the DeepSeek-V3.1 model, go to the Amazon Bedrock console, choose Model access under Bedrock configurations in the left navigation pane. To access the fully managed DeepSeek-V3.1 model, request access for DeepSeek-V3.1 in the DeepSeek section. You’ll then be granted access to the model in Amazon Bedrock.

Next, to test the DeepSeek-V3.1 model in Amazon Bedrock, choose Chat/Text under Playgrounds in the left menu pane. Then choose Select model in the upper left, and select DeepSeek as the category and DeepSeek-V3.1 as the model. Then choose Apply.

Using the selected DeepSeek-V3.1 model, I run the following prompt example about technical architecture decision.

Outline the high-level architecture for a scalable URL shortener service like bit.ly. Discuss key components like API design, database choice (SQL vs. NoSQL), how the redirect mechanism works, and how you would generate unique short codes.

You can turn the thinking on and off by toggling Model reasoning mode to generate a response’s chain of thought prior to the final conclusion.

You can also access the model using the AWS Command Line Interface (AWS CLI) and AWS SDK. This model supports both the InvokeModel and Converse API. You can check out a broad range of code examples for multiple use cases and a variety of programming languages.

To learn more, visit DeepSeek model inference parameters and responses in the AWS documentation.

Now available
DeepSeek-V3.1 is now available in the US West (Oregon), Asia Pacific (Tokyo), Asia Pacific (Mumbai), Europe (London), and Europe (Stockholm) AWS Regions. Check the full Region list for future updates. To learn more, check out the DeepSeek in Amazon Bedrock product page and the Amazon Bedrock pricing page.

Give the DeepSeek-V3.1 model a try in the Amazon Bedrock console today and send feedback to AWS re:Post for Amazon Bedrock or through your usual AWS Support contacts.

— Channy

AWS named as a Leader in 2025 Gartner Magic Quadrant for Cloud-Native Application Platforms and Container Management

Post Syndicated from Channy Yun (윤석찬) original https://aws.amazon.com/blogs/aws/aws-named-as-a-leader-in-2025-gartner-magic-quadrant-for-cloud-native-application-platforms-and-container-management/

A month ago, I shared that Amazon Web Services (AWS) is recognized as a Leader in 2025 Gartner Magic Quadrant for Strategic Cloud Platform Services (SCPS), with Gartner naming AWS a Leader for the fifteenth consecutive year.

In 2024, AWS was named as a Leader in the Gartner Magic Quadrant for AI Code Assistants, Cloud-Native Application Platforms, Cloud Database Management Systems, Container Management, Data Integration Tools, Desktop as a Service (DaaS), and Data Science and Machine Learning Platforms as well as the SCPS. In 2025, we were also recognized as a Leader in the Gartner Magic Quadrant for Contact Center as a Service (CCaaS), Desktop as a Service and Data Science and Machine Learning (DSML) platforms. We strongly believe this means AWS provides the broadest and deepest range of services to customers.

Today, I’m happy to share recent Magic Quadrant reports that named AWS as a Leader in more cloud technology markets: Cloud-Native Application Platforms (aka Cloud Application Platforms) and Container Management.

2025 Gartner Magic Quadrant for Cloud-Native Application Platforms
AWS has been named a Leader in the Gartner Magic Quadrant for Cloud-Native Application Platforms for 2 consecutive years. AWS was positioned highest on “Ability to Execute”. Gartner defines cloud-native application platforms as those that provide managed application runtime environments for applications and integrated capabilities to manage the lifecycle of an application or application component in the cloud environment.

The following image is the graphical representation of the 2025 Magic Quadrant for Cloud-Native Application Platforms.

Our comprehensive cloud-native application portfolio—AWS Lambda, AWS App Runner, AWS Amplify, and AWS Elastic Beanstalk—offers flexible options for building modern applications with strong AI capabilities, demonstrated through continued innovation and deep integration across our broader AWS service portfolio.

You can simplify the service selection through comprehensive documentation, reference architectures, and prescriptive guidance available in the AWS Solutions Library, along with AI-powered, contextual recommendations from Amazon Q based on your specific requirements. While AWS Lambda is optimized for AWS to provide the best possible serverless experience, it follows industry standards for serverless computing and supports common programming languages and frameworks. You can find all necessary capabilities within AWS, including advanced features for AI/ML, edge computing, and enterprise integration.

You can build, deploy, and scale generative AI agents and applications by integrating these compute offerings with Amazon Bedrock for serverless inferences and Amazon SageMaker for artificial intelligence and machine learning (AI/ML) training and management.

Access the complete 2025 Gartner Magic Quadrant for Cloud-Native Application Platforms to learn more.

2025 Gartner Magic Quadrant for Container Management
In the 2025 Gartner Magic Quadrant for Container Management, AWS has been named as a Leader for three years and was positioned furthest for “Completeness of Vision”. Gartner defines container management as offerings that support the deployment and operation of containerized workloads. This process involves orchestrating and overseeing the entire lifecycle of containers, covering deployment, scaling, and operations, to ensure their efficient and consistent performance across different environments.

The following image is the graphical representation of the 2025 Magic Quadrant for Container Management.

AWS container services offer fully managed container orchestration with AWS native solutions and open-source technologies to focus on providing a wide range of deployment options, from Kubernetes to our native orchestrator.

You can use Amazon Elastic Container Service (Amazon ECS) and Amazon Elastic Kubernetes Service (Amazon EKS). Both can be used with AWS Fargate for serverless container deployment. Additionally, EKS Auto Mode simplifies Kubernetes management by automatically provisioning infrastructure, selecting optimal compute instances, and dynamically scaling resources for containerized applications.

You can connect on-premises and edge infrastructure back to AWS container services with EKS Hybrid Nodes and ECS Anywhere, or use EKS Anywhere for a fully disconnected Kubernetes experience supported by AWS. With flexible compute and deployment options, you can reduce operational overhead and focus on innovation and drive business value faster.

Access the complete 2025 Gartner Magic Quadrant for Container Management to learn more.

— Channy

Gartner does not endorse any vendor, product or service depicted in its research publications and does not advise technology users to select only those vendors with the highest ratings or other designation. Gartner research publications consist of the opinions of Gartner’s research organization and should not be construed as statements of fact. Gartner disclaims all warranties, expressed or implied, with respect to this research, including any warranties of merchantability or fitness for a particular purpose.

GARTNER is a registered trademark and service mark of Gartner and Magic Quadrant is a registered trademark of Gartner, Inc. and/or its affiliates in the U.S. and internationally and are used herein with permission. All rights reserved.

AWS Weekly Roundup: Strands Agents 1M+ downloads, Cloud Club Captain, AI Agent Hackathon, and more (September 15, 2025)

Post Syndicated from Channy Yun (윤석찬) original https://aws.amazon.com/blogs/aws/aws-weekly-roundup-strands-agents-1m-downloads-cloud-club-captain-ai-agent-hackathon-and-more-september-15-2025/

Last week, Strands Agents, AWS open source for agentic AI SDK just hit 1 million downloads and earned 3,000+ GitHub Stars less than 4 months since launching as a preview in May 2025. With Strands Agents, you can build production-ready, multi-agent AI systems in a few lines of code.

We’ve continuously improved features including support for multi-agent patterns, A2A protocol, and Amazon Bedrock AgentCore. You can use a collection of sample implementations to help you get started with building intelligent agents using Strands Agents. We always welcome your contribution and feedback to our project including bug reports, new features, corrections, or additional documentation.

Here is the latest research article of Amazon Science about the future of agentic AI and questions that scientists are asking about agent-to-agent communications, contextual understanding, common sense reasoning, and more. You can understand the technical topic of agentic AI with with relatable examples, including one about our personal behaviors about leaving doors open or closed, locked or unlocked.

Last week’s launches
Here are some launches that got my attention:

  • Amazon EC2 M4 and M4 Pro Mac instances – New M4 Mac instances offer up to 20% better application build performance compared to M2 Mac instances, while M4 Pro Mac instances deliver up to 15% better application build performance compared to M2 Pro Mac instances. These instances are ideal for building and testing applications for Apple platforms such as iOS, macOS, iPadOS, tvOS, watchOS, visionOS, and Safari.
  • LocalStack integration in Visual Studio Code (VS Code) – You can use LocalStack to locally emulate and test your serverless applications using the familiar VS Code interface without switching between tools or managing complex setup, thus simplifying your local serverless development process.
  • AWS Cloud Development Kit (AWS CDK) Refactor (Preview) –You can rename constructs, move resources between stacks, and reorganize CDK applications while preserving the state of deployed resources. By using AWS CloudFormation’s refactor capabilities with automated mapping computation, CDK Refactor eliminates the risk of unintended resource replacement during code restructuring.
  • AWS CloudTrail MCP Server – New AWS CloudTrail MCP server allows AI assistants to analyze API calls, track user activities, and perform advanced security analysis across your AWS environment through natural language interactions. You can explore more AWS MCP servers for working with AWS service resources.
  • Amazon CloudFront support for IPv6 origins – Your applications can send IPv6 traffic all the way to their origins, allowing them to meet their architectural and regulatory requirements for IPv6 adoption. End-to-end IPv6 support improves network performance for end users connecting over IPv6 networks, and also removes concerns for IPv4 address exhaustion for origin infrastructure.

For a full list of AWS announcements, be sure to keep an eye on the What’s New with AWS? page.

Other AWS news
Here are some additional news items that you might find interesting:

  • A city in the palm of your hand – Check out this interactive feature that explains how our AWS Trainium chip designers think like city planners, optimizing every nanometer to move data at near light speed.
  • Measuring the effectiveness of software development tools and practices – Read how Amazon developers that identified specific challenges before adopting AI tools cut costs by 15.9% year-over-year using our cost-to-serve-software framework (CTS-SW). They deployed more frequently and reduced manual interventions by 30.4% by focusing on the right problems first.
  • Become an AWS Cloud Club Captain – Join a growing network of student cloud enthusiasts by becoming an AWS Cloud Club Captain! As a Captain, you’ll get to organize events and building cloud communities while developing leadership skills. Application window is open September 1-28, 2025.

Upcoming AWS events
Check your calendars and sign up for these upcoming AWS events as well as AWS re:Invent and AWS Summits:

  • AWS AI Agent Global Hackathon – This is your chance to dive deep into our powerful generative AI stack and create something truly awesome. From September 8 to October 20, you have the opportunity to create AI agents using AWS suite of AI services, competing for over $45,000 in prizes and exclusive go-to-market opportunities.
  • AWS Gen AI Lofts – You can learn AWS AI products and services with exclusive sessions and meet industry-leading experts, and have valuable networking opportunities with investors and peers. Register in your nearest city: Mexico City (September 30–October 2), Paris (October 7–21), London (Oct 13–21), and Tel Aviv (November 11–19).
  • 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: Aotearoa and Poland (September 18), South Africa (September 20), Bolivia (September 20), Portugal (September 27), Germany (October 7), and Hungary (October 16).

You can browse all upcoming AWS events and AWS startup events.

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

— Channy

New general-purpose Amazon EC2 M8i and M8i Flex instances are now available

Post Syndicated from Channy Yun (윤석찬) original https://aws.amazon.com/blogs/aws/new-general-purpose-amazon-ec2-m8i-and-m8i-flex-instances-are-now-available/

Today, we’re announcing the general availability of Amazon Elastic Compute Cloud (Amazon EC2) general-purpose M8i and M8i-Flex instances powered by custom Intel Xeon 6 processors available only on AWS with sustained all-core 3.9 GHz turbo frequency. These instances deliver the highest performance and fastest memory bandwidth among comparable Intel processors in the cloud. They also deliver up to 15 percent better price performance, up to 20 percent higher performance, and 2.5 times more memory bandwidth compared to previous generation M7i and M7i-Flex instances.

M8i and M8i-flex instances are ideal for running general purpose workloads such as general web application servers, virtual desktops, batch processing, microservices, databases, and enterprise applications. In terms of performance, these instances are specifically up to 60 percent faster for NGINX web applications, up to 30 percent faster for PostgreSQL database workloads, and up to 40 percent faster for AI deep learning recommendation models compared to M7i and M7i-Flex instances.

As like R8i and R8i-Flex instances, these instances use the new sixth generation AWS Nitro Cards, delivering up to two times more network and Amazon Elastic Block Storage (Amazon EBS) bandwidth compared to the previous generation instances. It greatly improves network throughput for workloads handling small packets such as web, application, and gaming servers. They also support bandwidth configuration with 25 percent allocation adjustments between network and Amazon EBS bandwidth, enabling better database performance, query processing, and logging speeds.

M8i instances
M8i instances provide up to 384 vCPUs and 1.5 TB memory including bare metal instances that provide dedicated access to the underlying physical hardware. These SAP-certified instances help you to run large application servers and databases, gaming servers, CPU-based inference, and video streaming that need the largest instance sizes or high CPU continuously.

Here are the specs for M8i instances:

Instance size vCPUs Memory (GiB) Network bandwidth (Gbps) EBS bandwidth (Gbps)
m8i.large 2 8 Up to 12.5 Up to 10
m8i.xlarge 4 16 Up to 12.5 Up to 10
m8i.2xlarge 8 32 Up to 15 Up to 10
m8i.4xlarge 16 64 Up to 15 Up to 10
m8i.8xlarge 32 128 15 10
m8i.12xlarge 48 192 22.5 15
m8i.16xlarge 64 256 30 20
m8i.24xlarge 96 384 40 30
m8i.32xlarge 128 512 50 40
m8i.48xlarge 192 768 75 60
m8i.96xlarge 384 1536 100 80
m8i.metal-48xl 192 768 75 60
m8i.metal-96xl 384 1536 100 80

M8i-Flex instances
M8i-Flex instances are a lower-cost variant of the M8i instances, with 5 percent better price performance at 5 percent lower prices. They’re designed for workloads that benefit from the latest generation performance but don’t fully utilize all compute resources. These instances can reach up to the full CPU performance 95 percent of the time.

Here are the specs for the M8i-Flex instances:

Instance size vCPUs Memory (GiB) Network bandwidth (Gbps) EBS bandwidth (Gbps)
m8i-flex.large 2 8 Up to 12.5 Up to 10
m8i-flex.xlarge 4 16 Up to 12.5 Up to 10
m8i-flex.2xlarge 8 32 Up to 15 Up to 10
m8i-flex.4xlarge 16 64 Up to 15 Up to 10
m8i-flex.8xlarge 32 128 Up to 15 Up to 10
m8i-flex.12xlarge 48 192 Up to 22.5 Up to 15
m8i-flex.16xlarge 64 256 Up to 30 Up to 20

If you’re currently using earlier generations of general-purpose instances, you can adopt M8i-Flex instances without having to make changes to your application or your workload.

Now available
Amazon EC2 M8i and M8i-Flex instances are available today in the US East (N. Virginia), US East (Ohio), US West (Oregon), and Europe (Spain) AWS Regions. M8i and M8i-Flex instances can be purchased as On-Demand, Savings Plan, and Spot instances. M8i instances are also available in Dedicated Instances and Dedicated Hosts. To learn more, visit the Amazon EC2 Pricing page.

Give M8i and M8i-Flex instances a try in the Amazon EC2 console. To learn more, visit the Amazon EC2 M8i instances page and send feedback to AWS re:Post for EC2 or through your usual AWS Support contacts.

— Channy

AWS services scale to new heights for Prime Day 2025: key metrics and milestones

Post Syndicated from Channy Yun (윤석찬) original https://aws.amazon.com/blogs/aws/aws-services-scale-to-new-heights-for-prime-day-2025-key-metrics-and-milestones/

Amazon Prime Day 2025 was the biggest Amazon Prime Day shopping event ever, setting records for both sales volume and total items sold during the 4-day event. Prime members saved billions while shopping Amazon’s millions of deals during the event.

This year marked a significant transformation in the Prime Day experience through advancements in the generative AI offerings from Amazon and AWS. Customers used Alexa+—the Amazon next-generation personal assistant now available in early access to millions of customers—along with the AI-powered shopping assistant, Rufus, and AI Shopping Guides. These features, built on more than 15 years of cloud innovation and machine learning expertise from AWS, combined with deep retail and consumer experience from Amazon, helped customers quickly discover deals and get product information, complementing the fast, free delivery that Prime members enjoy year-round.

As part of our annual tradition to tell you about how AWS powered Prime Day for record-breaking sales, I want to share the services and chart-topping metrics from AWS that made your amazing shopping experience possible.


Prime Day 2025 – all the numbers
During the weeks leading up to big shopping events like Prime Day, Amazon fulfillment centers and delivery stations work to get ready and ensure operations run efficiently and safely. For example, the Amazon automated storage and retrieval system (ASRS) operates a global fleet of industrial mobile robots that move goods around Amazon fulfillment centers.

AWS Outposts, a fully managed service that extends the AWS experience on-premises, powers software applications that manage the command-and-control of Amazon ASRS and supports same-day and next-day deliveries through low-latency processing of critical robotic commands.

During Prime Day 2025, AWS Outposts at one of the largest Amazon fulfillment centers sent more than 524 million commands to over 7,000 robots, reaching peak volumes of 8 million commands per hour—a 160 percent increase compared to Prime Day 2024.

Here are some more interesting, mind-blowing metrics:

  • Amazon Elastic Compute Cloud (Amazon EC2) – During Prime Day 2025, AWS Graviton, a family of processors designed to deliver the best price performance for cloud workloads running in Amazon EC2, powered more than 40 percent of the Amazon EC2 compute used by Amazon.com. Amazon also deployed over 87,000 AWS Inferentia and AWS Trainium chips – custom silicon chips for deep learning and generative AI training and inference – to power Amazon Rufus for Prime Day.
  • Amazon SageMaker AI — Amazon SageMaker AI, a fully managed service that brings together a broad set of tools to enable high-performance, low-cost machine learning (ML), processed more than 626 billion inference requests during Prime Day 2025.
  • Amazon Elastic Container Service (Amazon ECS) and AWS Fargate– Amazon Elastic Container Service (Amazon ECS) is a fully managed container orchestration service that works seamlessly with AWS Fargate, a serverless compute engine for containers. During Prime Day 2025, Amazon ECS launched an average of 18.4 million tasks per day on AWS Fargate, representing a 77 percent increase from the previous year’s Prime Day average.
  • AWS Fault Injection Service (AWS FIS) – We ran over 6,800 AWS FIS experiments—over eight times more than we conducted in 2024—to test resilience and ensure Amazon.com remains highly available on Prime Day. This significant increase was made possible by two improvements: new Amazon ECS support for network fault injection experiments on AWS Fargate, and the integration of FIS testing in continuous integration and continuous delivery (CI/CD) pipelines.
  • AWS Lambda – AWS Lambda, a serverless compute service that lets you run code without managing infrastructure, handled over 1.7 trillion invocations per day during Prime Day 2025.
  • Amazon API Gateway – During Prime Day 2025, Amazon API Gateway, a fully managed service that makes it easy to create, maintain, and secure APIs at any scale, processed over 1 trillion internal service requests—a 30 percent increase in requests on average per day compared to Prime Day 2024.
  • Amazon CloudFront – Amazon CloudFront, a content delivery network (CDN) service that securely delivers content with low latency and high transfer speeds, delivered over 3 trillion HTTP requests during the global week of Prime Day 2025, a 43 percent increase in requests compared to Prime Day 2024.
  • Amazon Elastic Block Store (Amazon EBS) – During Prime Day 2025, Amazon EBS, our high-performance block storage service, peaked at 20.3 trillion I/O operations, moving up to an exabyte of data daily.
  • Amazon Aurora – On Prime Day, Amazon Aurora, a relational database management system (RDBMS) built for high performance and availability at global scale for PostgreSQL, MySQL, and DSQL, processed 500 billion transactions, stored 4,071 terabytes of data, and transferred 999 terabytes of data.
  • Amazon DynamoDB – Amazon DynamoDB, a serverless, fully managed, distributed NoSQL database, powers multiple high-traffic Amazon properties and systems including Alexa, the Amazon.com sites, and all Amazon fulfillment centers. Over the course of Prime Day, these sources made tens of trillions of calls to the DynamoDB API. DynamoDB maintained high availability while delivering single-digit millisecond responses and peaking at 151 million requests per second.
  • Amazon ElastiCache – During Prime Day, Amazon ElastiCache, a fully managed caching service delivering microsecond latency, peaked at serving over 1.5 quadrillion daily requests and over 1.4 trillion requests in a minute.
  • Amazon Kinesis Data Streams – Amazon Kinesis Data Streams, a fully managed serverless data streaming service, processed a peak of 807 million records per second during Prime Day 2025.
  • Amazon Simple Queue Service (Amazon SQS) – During Prime Day 2025, Amazon SQS – a fully managed message queuing service for microservices, distributed systems, and serverless applications – set a new peak traffic record of 166 million messages per second.
  • Amazon GuardDuty – During Prime Day 2025, Amazon GuardDuty, an intelligent threat detection service, monitored an average of 8.9 trillion log events per hour, a 48.9 percent increase from last year’s Prime Day.
  • AWS CloudTrail – AWS CloudTrail, which tracks user activity and API usage on AWS, as well as in hybrid and multicloud environments, processed over 2.5 trillion events during Prime Day 2025, compared to 976 billion events in 2024.

Prepare to scale
If you’re preparing for similar business-critical events, product launches, and migrations, I recommend that you take advantage of our newly branded AWS Countdown (formerly known as AWS Infrastructure Event Management, or IEM). This comprehensive support program helps assess operational readiness, identify and mitigate risks, and plan capacity, using proven playbooks developed by AWS experts. We’ve expanded to include: generative AI implementation support to help you confidently launch and scale AI initiatives; migration and modernization support, including mainframe modernization; and infrastructure optimization for specialized sectors including election systems, retail operations, healthcare services, and sports and gaming events.

I look forward to seeing what other records will be broken next year!

— Channy

AWS named as a Leader in 2025 Gartner Magic Quadrant for Strategic Cloud Platform Services for 15 years in a row

Post Syndicated from Channy Yun (윤석찬) original https://aws.amazon.com/blogs/aws/aws-named-as-a-leader-in-2025-gartner-magic-quadrant-for-strategic-cloud-platform-services-for-15-years-in-a-row/

On August 4, 2025, Gartner published its Gartner Magic Quadrant for Strategic Cloud Platform Services (SCPS). Amazon Web Services (AWS) is the longest-running Magic Quadrant Leader, with Gartner naming AWS a Leader for the fifteenth consecutive year.

In the report, Gartner once again placed AWS highest on the “Ability to Execute” axis. We believe this reflects our ongoing commitment to giving customers the broadest and deepest set of capabilities to accelerate innovation as well as unparalleled security, reliability, and performance they can trust for their most critical applications.

Here is the graphical representation of the 2025 Magic Quadrant for Strategic Cloud Platform Services.

Gartner recognized AWS strengths as:

  • Largest cloud community – AWS has built a strong global community of cloud professionals, providing significant opportunities for learning and engagement.
  • Cloud-inspired silicon – AWS has used its cloud computing experience to develop custom silicon designs, including AWS Graviton, AWS Inferentia, and AWS Trainium, which enable tighter integration between hardware and software, improved power efficiency, and greater control over supply chains.
  • Global scale and operational execution – AWS’s significant share of global cloud market revenue has enabled it to build a larger and more robust network of integration partners than some other providers in this analysis, which in turn helps organizations successfully adopt cloud.

The most common feedback I hear from customers is that AWS has the largest and most dynamic cloud community, making it easy to ask questions and learn from millions of active customers and tens of thousands of partners globally. We recently launched our community hub, AWS Builder Center to connect directly with AWS Heroes and AWS Community Builders. You can also explore and join AWS User Groups and AWS Cloud Clubs in a city near you.

We have also focused on facilitating the digital transformation of enterprise customers through a number of enterprise programs, such as the AWS Migration Acceleration Program. Using generative AI on migration and modernization, we introduced AWS Transform, the first agentic AI service developed to accelerate enterprise modernization of mission-critical business workloads such as .NET, mainframe, and VMware.

Access the complete full Gartner report to learn more. It outlines the methodology and evaluation criteria used to develop their assessments of each cloud service provider included in the report. This report can serve as a guide when choosing a cloud provider that helps you innovate on behalf of your customers.

— Channy

Gartner does not endorse any vendor, product or service depicted in its research publications and does not advise technology users to select only those vendors with the highest ratings or other designation. Gartner research publications consist of the opinions of Gartner’s research organization and should not be construed as statements of fact. Gartner disclaims all warranties, expressed or implied, with respect to this research, including any warranties of merchantability or fitness for a particular purpose.

GARTNER is a registered trademark and service mark of Gartner and Magic Quadrant is a registered trademark of Gartner, Inc. and/or its affiliates in the U.S. and internationally and are used herein with permission. All rights reserved.

Amazon DocumentDB Serverless is now available

Post Syndicated from Channy Yun (윤석찬) original https://aws.amazon.com/blogs/aws/amazon-documentdb-serverless-is-now-available/

Today, we’re announcing the general availability of Amazon DocumentDB Serverless, a new configuration for Amazon DocumentDB (with MongoDB compatibility) that automatically scales compute and memory based on your application’s demand. Amazon DocumentDB Serverless simplifies database management with no upfront commitments or additional costs, offering up to 90 percent cost savings compared to provisioning for peak capacity.

With Amazon DocumentDB Serverless, you can use the same MongoDB compatible-APIs and capabilities as Amazon DocumentDB, including read replicas, Performance Insights, I/O optimized, and integrations with other Amazon Web Services (AWS) services.

Amazon DocumentDB Serverless introduces a new database configuration measured in a DocumentDB Capacity Unit (DCU), a combination of approximately 2 gibibytes (GiB) of memory, corresponding CPU, and networking. It continually tracks utilization of resources such as CPU, memory, and network coming from database operations performed by your application.

Amazon DocumentDB Serverless automatically scales DCUs up or down to meet demand without disrupting database availability. Switching from provisioned instances to serverless in an existing cluster is as straightforward as adding or changing the instance type. This transition doesn’t require any data migration. To learn more, visit How Amazon DocumentDB Serverless works.

Some key use cases and advantages of Amazon DocumentDB Serverless include:

  • Variable workloads – With Amazon DocumentDB Serverless, you can handle sudden traffic spikes such as periodic promotional events, development and testing environments, and new applications where usage might ramp up quickly. You can also build agentic AI applications that benefit from built-in vector search for Amazon DocumentDB and serverless adaptability to handle dynamically invoked agentic AI workflows.
  • Multi-tenant workloads – You can use Amazon DocumentDB Serverless to manage individual database capacity across the entire database fleet. You don’t need to manage hundreds or thousands of databases for enterprises applications or multi-tenant environments of a software as a service (SaaS) vendor.
  • Mixed-use workloads – You can balance read and write capacity in workloads that periodically experience spikes in query traffic, such as online transaction processing (OLTP) applications. By specifying promotion tiers for Amazon DocumentDB Serverless instances in a cluster, you can configure your cluster so that the reader instances can scale independently of the writer instance to handle the additional load.

For steady workloads, Amazon DocumentDB provisioned instances are more suitable. You can select an instance class that offers a predefined amount of memory, CPU power, and I/O bandwidth. If your workload changes when using provisioned instances, you should manually modify the instance class of your writer and readers. Optionally, you can add serverless instances to an existing provisioned Amazon DocumentDB cluster at any time.

Amazon DocumentDB Serverless in action
To get started with Amazon DocumentDB Serverless, go to the Amazon DocumentDB console. In the left navigation pane, choose Clusters and Create.

On the Create Amazon DocumentDB cluster page, choose Instance-based cluster type and then Serverless instance configuration. You can choose minimum and maximum capacity DCUs. Amazon DocumentDB Serverless is supported starting with Amazon DocumentDB 5.0.0 and higher with a capacity range of 0.5–256 DCUs.

If you use features such as auditing and Performance Insights, consider adding DCUs for each feature. To learn more, visit Amazon DocumentDB Serverless scaling configuration.

To add a serverless instance to an existing provisioned cluster, choose Add instances on the Actions menu when you choose the provisioned cluster. If you use a cluster with an earlier version such as 3.6 or 4.0, you should first upgrade the cluster to the supported engine version (5.0).

On the Add instances page, choose Serverless in the DB instance class section for each new serverless instance you want to create. To add another instance, choose Add instance and continue adding instances until you have reached the desired number of new instances. Choose Create.

You can perform a failover operation to make a DocumentDB Serverless instance the cluster writer. Also, you can convert any remaining provisioned Amazon DocumentDB instances to DocumentDB Serverless instances by changing an instance’s class or removing them from the cluster by deleting an Amazon DocumentDB instance.

Now, you can connect to your Amazon DocumentDB cluster using AWS CloudShell. Choose Connect to cluster, and you can see the AWS CloudShell Run command screen. Enter a unique name in New environment name and choose Create and run.

When prompted, enter the password for the Amazon DocumentDB cluster. You’re successfully connected to your Amazon DocumentDB cluster, and you can run a few queries to get familiar with using a document database.

To learn more, visit Creating a cluster that uses Amazon DocumentDB Serverless and Managing Amazon DocumentDB Serverless in the AWS documentation.

Now available
Amazon DocumentDB Serverless is now available starting with Amazon DocumentDB 5.0 for both new and existing clusters. You only pay a flat rate per second of DCU usage. To learn more about pricing details and Regional availability, visit the Amazon DocumentDB pricing page.

Give these new features a try in the Amazon DocumentDB console and send feedback to AWS re:Post for Amazon DocumentDB or through your usual AWS Support contacts.

— Channy

AWS Free Tier update: New customers can get started and explore AWS with up to $200 in credits

Post Syndicated from Channy Yun (윤석찬) original https://aws.amazon.com/blogs/aws/aws-free-tier-update-new-customers-can-get-started-and-explore-aws-with-up-to-200-in-credits/

When you’re new to Amazon Web Services (AWS), you can get started with AWS Free Tier to learn about AWS services, gain hands-on experience, and build applications. You can explore the portfolio of services without incurring costs, making it even easier to get started with AWS.

Today, we’re announcing some enhancements to the AWS Free Tier program, offering up to $200 in AWS credits that can be used across AWS services. You’ll receive $100 in AWS credits upon sign-up and can earn an additional $100 in credits by using services such as Amazon Elastic Compute Cloud (Amazon EC2), Amazon Relational Database Service (Amazon RDS), AWS Lambda, Amazon Bedrock, and AWS Budgets.

The enhanced AWS Free Tier program offers two options during sign-up: a free account plan and a paid account plan. The free account plan ensures you won’t incur any charges until you upgrade to a paid plan. The free account plan expires after 6 months or when you exhaust your credits, whichever comes first.

While on the free account plan, you won’t be able to use some services typically used by large enterprises. You can upgrade to a paid plan at any time to continue building on AWS. When you upgrade, you can still use any unused credits for any eligible service usage for up to 12 months from your initial sign-up date.

When you choose the paid plan, AWS will automatically apply your Free Tier credits to the use of eligible services in your AWS bills. For usage that exceeds the credits, you’re charged with the on-demand pricing.

Get up to $200 credits in action
When you sign up for either a free plan or a paid plan, you’ll receive $100 credit. You can also earn an additional $20 credits for each of these five AWS service activities you complete:

  • Amazon EC2 – You’ll learn how to launch an EC2 instance and terminate it.
  • Amazon RDS – You’ll learn the basic configuration options for launching an RDS database.
  • AWS Lambda – You’ll learn to build a straightforward web application consisting of a Lambda function with a function URL.
  • Amazon Bedrock – You’ll learn how to submit a prompt to generate a response in the Amazon Bedrock text playground.
  • AWS Budgets – You’ll learn how to set a budget that alerts you when you exceed your budgeted cost amount.

You can see the credit details in the Explore AWS widget in the AWS Management Console.

These activities are designed to expose customers to important building blocks of AWS, including cost and usage that show up in the AWS Billing Console. These charges are deducted from your Free Tier credits and help teach new AWS users about selecting the appropriate instance sizes to minimize your costs.

Choose Set up a cost budget using AWS Budgets to earn your first $20 credits. It redirects to the AWS Billing and Cost Management console.

To create your first budget, choose Use a template (simplified) and Monthly cost budget to notify you if you exceed, or are forecasted to exceed, the budget amount.

When you choose the Customize (advanced) setup option, you can customize a budget to set parameters specific to your use case, scope of AWS services or AWS Regions, the time period, the start month, and specific accounts.

After you successfully create your budget, your begin receiving alerts when your spend exceeds your budgeted amount.

You can go to the Credits page in the left navigation pane in the AWS Billing and Cost Management Console to confirm your $20 in credits. Please note, it can take up to 10 minutes for your credits to appear.

You can receive an additional $80 by completing the remaining four activities. Now you can use up to $200 in credits to learn AWS services and build your first application.

Things to know
Here are some of things to know about the enhanced AWS Free Tier program:

  • Notifications – We’ll send an email alert when 50 percent, 25 percent, or 10 percent of your AWS credits remain. We’ll also send notifications to the AWS console and your email inbox when you have 15 days, 7 days, and 2 days left in your 6-month free period. After your free period ends, we’ll send you an email with instructions on how to upgrade to a paid plan. You’ll have 90 days to reopen your account by upgrading to a paid plan.
  • AWS services – The free account can access parts of AWS services including over 30 services that offer always-free tier. The paid account can access all AWS services. For more information, visit AWS Free Tier page.
  • Legacy Free Tier – If your AWS account was created before July 15, 2025, you’ll continue to be in the legacy Free Tier program, where you can access short-term trials, 12-month trials, and always free tier services. The always-free tier is available under both the new Free Tier program and the legacy Free Tier program.

Now available
The new AWS Free Tier features are generally available in all AWS Regions, except the AWS GovCloud (US) Regions and the China Regions. To learn more, visit the AWS Free Tier page and AWS Free Tier Documentation.

Give the new AWS Free Tier a try by signing up today, and send feedback to AWS re:Post for AWS Free Tier or through your usual AWS Support contacts.

— Channy

Introducing Amazon S3 Vectors: First cloud storage with native vector support at scale (preview)

Post Syndicated from Channy Yun (윤석찬) original https://aws.amazon.com/blogs/aws/introducing-amazon-s3-vectors-first-cloud-storage-with-native-vector-support-at-scale/

Today, we’re announcing the preview of Amazon S3 Vectors, a purpose-built durable vector storage solution that can reduce the total cost of uploading, storing, and querying vectors by up to 90 percent. Amazon S3 Vectors is the first cloud object store with native support to store large vector datasets and provide subsecond query performance that makes it affordable for businesses to store AI-ready data at massive scale.

Vector search is an emerging technique used in generative AI applications to find similar data points to given data by comparing their vector representations using distance or similarity metrics. Vectors are numerical representation of unstructured data created from embedding models. You generate vectors using embedding models for fields inside your document and store vectors into S3 Vectors to search semantically.

S3 Vectors introduces vector buckets, a new bucket type with a dedicated set of APIs to store, access, and query vector data without provisioning any infrastructure. When you create an S3 vector bucket, you organize your vector data within vector indexes, making it simple for running similarity search queries against your dataset. Each vector bucket can have up to 10,000 vector indexes, and each vector index can hold tens of millions of vectors.

After creating a vector index, when adding vector data to the index, you can also attach metadata as key-value pairs to each vector to filter future queries based on a set of conditions, for example, dates, categories, or user preferences. As you write, update, and delete vectors over time, S3 Vectors automatically optimizes the vector data to achieve the best possible price-performance for vector storage, even as the datasets scale and evolve.

S3 Vectors is also natively integrated with Amazon Bedrock Knowledge Bases, including within Amazon SageMaker Unified Studio, for building cost-effective Retrieval-Augmented Generation (RAG) applications. Through its integration with Amazon OpenSearch Service, you can lower storage costs by keeping infrequent queried vectors in S3 Vectors and then quickly move them to OpenSearch as demands increase or to support real-time, low-latency search operations.

With S3 Vectors, you can now economically store the vector embeddings that represent massive amounts of unstructured data such as images, videos, documents, and audio files, enabling scalable generative AI applications including semantic and similarity search, RAG, and build agent memory. You can also build applications to support a wide range of industry use cases including personalized recommendations, automated content analysis, and intelligent document processing without the complexity and cost of managing vector databases.

S3 Vectors in action
To create a vector bucket, choose Vector buckets in the left navigation pane in the Amazon S3 console and then choose Create vector bucket.

Enter a vector bucket name and choose the encryption type. If you don’t specify an encryption type, Amazon S3 applies server-side encryption with Amazon S3 managed keys (SSE-S3) as the base level of encryption for new vectors. You can also choose server-side encryption with AWS Key Management Service (AWS KMS) keys (SSE-KMS). To learn more about managing your vector bucket, visit S3 Vector buckets in the Amazon S3 User Guide.

Now, you can create a vector index to store and query your vector data within your created vector bucket.

Enter a vector index name and the dimensionality of the vectors to be inserted in the index. All vectors added to this index must have exactly the same number of values.

For Distance metric, you can choose either Cosine or Euclidean. When creating vector embeddings, select your embedding model’s recommended distance metric for more accurate results.

Choose Create vector index and then you can insert, list, and query vectors.

To insert your vector embeddings to a vector index, you can use the AWS Command Line Interface (AWS CLI), AWS SDKs, or Amazon S3 REST API. To generate vector embeddings for your unstructured data, you can use embedding models offered by Amazon Bedrock.

If you’re using the latest AWS Python SDKs, you can generate vector embeddings for your text using Amazon Bedrock using following code example:

# Generate and print an embedding with Amazon Titan Text Embeddings V2.
import boto3 
import json 

# Create a Bedrock Runtime client in the AWS Region of your choice. 
bedrock= boto3.client("bedrock-runtime", region_name="us-west-2") 

The text strings to convert to embeddings.
texts = [
"Star Wars: A farm boy joins rebels to fight an evil empire in space", 
"Jurassic Park: Scientists create dinosaurs in a theme park that goes wrong",
"Finding Nemo: A father fish searches the ocean to find his lost son"]

embeddings=[]
#Generate vector embeddings for the input texts
for text in texts:
        body = json.dumps({
            "inputText": text
        })    
        # Call Bedrock's embedding API
        response = bedrock.invoke_model(
        modelId='amazon.titan-embed-text-v2:0',  # Titan embedding model 
        body=body)   
        # Parse response
        response_body = json.loads(response['body'].read())
        embedding = response_body['embedding']
        embeddings.append(embedding)

Now, you can insert vector embeddings into the vector index and query vectors in your vector index using the query embedding:

# Create S3Vectors client
s3vectors_client = boto3.client('s3vectors', region_name='us-west-2')

# Insert vector embedding
s3vectors.put_vectors( vectorBucketName="channy-vector-bucket",
  indexName="channy-vector-index", 
  vectors=[
{"key": "v1", "data": {"float32": embeddings[0]}, "metadata": {"id": "key1", "source_text": texts[0], "genre":"scifi"}},
{"key": "v2", "data": {"float32": embeddings[1]}, "metadata": {"id": "key2", "source_text": texts[1], "genre":"scifi"}},
{"key": "v3", "data": {"float32": embeddings[2]}, "metadata": {"id": "key3", "source_text":  texts[2], "genre":"family"}}
],
)

#Create an embedding for your query input text
# The text to convert to an embedding.
input_text = "List the movies about adventures in space"

# Create the JSON request for the model.
request = json.dumps({"inputText": input_text})

# Invoke the model with the request and the model ID, e.g., Titan Text Embeddings V2. 
response = bedrock.invoke_model(modelId="amazon.titan-embed-text-v2:0", body=request)

# Decode the model's native response body.
model_response = json.loads(response["body"].read())

# Extract and print the generated embedding and the input text token count.
embedding = model_response["embedding"]

# Performa a similarity query. You can also optionally use a filter in your query
query = s3vectors.query_vectors( vectorBucketName="channy-vector-bucket",
  indexName="channy-vector-index",
  queryVector={"float32":embedding},
  topK=3, 
  filter={"genre":"scifi"},
  returnDistance=True,
  returnMetadata=True
  )
results = query["vectors"]
print(results)

To learn more about inserting vectors into a vector index, or listing, querying, and deleting vectors, visit S3 vector buckets and S3 vector indexes in the Amazon S3 User Guide. Additionally, with the S3 Vectors embed command line interface (CLI), you can create vector embeddings for your data using Amazon Bedrock and store and query them in an S3 vector index using single commands. For more information, see the S3 Vectors Embed CLI GitHub repository.

Integrate S3 Vectors with other AWS services
S3 Vectors integrates with other AWS services such as Amazon Bedrock, Amazon SageMaker, and Amazon OpenSearch Service to enhance your vector processing capabilities and provide comprehensive solutions for AI workloads.

Create Amazon Bedrock Knowledge Bases with S3 Vectors
You can use S3 Vectors in Amazon Bedrock Knowledge Bases to simplify and reduce the cost of vector storage for RAG applications. When creating a knowledge base in the Amazon Bedrock console, you can choose the S3 vector bucket as your vector store option.

In Step 3, you can choose the Vector store creation method either to create an S3 vector bucket and vector index or choose the existing S3 vector bucket and vector index that you’ve previously created.

For detailed step-by-step instructions, visit Create a knowledge base by connecting to a data source in Amazon Bedrock Knowledge Bases in the Amazon Bedrock User Guide.

Using Amazon SageMaker Unified Studio
You can create and manage knowledge bases with S3 Vectors in Amazon SageMaker Unified Studio when you build your generative AI applications through Amazon Bedrock. SageMaker Unified Studio is available in the next generation of Amazon SageMaker and provides a unified development environment for data and AI, including building and texting generative AI applications that use Amazon Bedrock knowledge bases.

You can choose your knowledge bases using the S3 Vectors created through Amazon Bedrock when you build generative AI applications. To learn more, visit Add a data source to your Amazon Bedrock app in the Amazon SageMaker Unified Studio User Guide.

Export S3 vector data to Amazon OpenSearch Service
You can balance cost and performance by adopting a tiered strategy that stores long-term vector data cost-effectively in Amazon S3 while exporting high priority vectors to OpenSearch for real-time query performance.

This flexibility means your organizations can access OpenSearch’s high performance (high QPS, low latency) for critical, real-time applications, such as product recommendations or fraud detection, while keeping less time-sensitive data in S3 Vectors.

To export your vector index, choose Advanced search export, then choose Export to OpenSearch in the Amazon S3 console.

Then, you will be brought to the Amazon OpenSearch Service Integration console with a template for S3 vector index export to OpenSearch vector engine. Choose Export with pre-selected S3 vector source and a service access role.

It will start the steps to create a new OpenSearch Serverless collection and migrate data from your S3 vector index into an OpenSearch knn index.

Choose the Import history in the left navigation pane. You can see the new import job that was created to make a copy of vector data from your S3 vector index into the OpenSearch Serverless collection.

Once the status changes to Complete, you can connect to the new OpenSearch serverless collection and query your new OpenSearch knn index.

To learn more, visit Creating and managing Amazon OpenSearch Serverless collections in the Amazon OpenSearch Service Developer Guide.

Now available
Amazon S3 Vectors, and its integrations with Amazon Bedrock, Amazon OpenSearch Service, and Amazon SageMaker are now in preview in the US East (N. Virginia), US East (Ohio), US West (Oregon), Europe (Frankfurt), and Asia Pacific (Sydney) Regions.

Give S3 Vectors a try in the Amazon S3 console today and send feedback to AWS re:Post for Amazon S3 or through your usual AWS Support contacts.

— Channy

TwelveLabs video understanding models are now available in Amazon Bedrock

Post Syndicated from Channy Yun (윤석찬) original https://aws.amazon.com/blogs/aws/twelvelabs-video-understanding-models-are-now-available-in-amazon-bedrock/

Earlier this year, we preannounced that TwelveLabs video understanding models were coming to Amazon Bedrock. Today, we’re announcing the models are now available for searching through videos, classifying scenes, summarizing, and extracting insights with precision and reliability.

TwelveLabs has introduced Marengo, a video embedding model proficient at performing tasks such as search and classification, and Pegasus, a video language model that can generate text based on video data. These models are trained on Amazon SageMaker HyperPod to deliver groundbreaking video analysis that provides text summaries, metadata generation, and creative optimization.

With the TwelveLabs models in Amazon Bedrock, you can find specific moments using natural language video search capabilities like “show me the first touchdown of the game” or “find the scene where the main characters first meet” and instantly jump to those exact moments. You can also build applications to understand video content by generating descriptive text such as titles, topics, hashtags, summaries, chapters, or highlights for discovering insights and connections without requiring predefined labels or categories.

For example, you can find recurring themes in customer feedback or spot product usage patterns that weren’t obvious before. Whether you have hundreds or thousands of hours of video content, you can now transform that entire library into a searchable knowledge resource while maintaining enterprise-grade security and performance.

Let’s take a look at the Marengo and Pegasus videos that TwelveLabs has published.

You can transform video workflows with these models across industries. Media producers and editors can instantly locate specific scenes or dialogue, which means you can focus on storytelling rather than sifting through hours of footage. Marketing teams are streamlining their advertising workflows by quickly personalizing content to resonate with various audiences, while security teams are using the technology to proactively identify potential risks by spotting patterns across multiple video feeds.

Getting started with TwelveLabs models in Amazon Bedrock
Before getting started, if you’re new to using TwelveLabs models, go to the Amazon Bedrock console and choose Model access in the bottom left navigation pane. To access the latest TwelveLabs models, request access for Marengo Embed 2.7 and Pegasus 1.2 in TwelveLabs.

To use TwelveLabs models in Amazon Bedrock, choose Chat/Text Playground under Test in the left navigation pane. Choose Select model, select TwelveLabs as the category and Pegasus as the model, and then choose Apply.

For searching or generating text from your video, you should upload your video to an Amazon Simple Storage Service (Amazon S3) bucket or input Base64-formatted video string.

I will use a sample video generated with Amazon Nova Reel with a prompt: “A cute raccoon playing guitar underwater.”

Enter the S3 URI of your uploaded video and the S3 Bucket Owner, and then run your prompt: “Tell me about the video by timeline”.

You can get following model response:

The video opens with an underwater scene featuring a sandy seabed and swaying seaweed, creating a serene atmosphere. At around 5 seconds, a raccoon appears, swimming towards the camera with a curious expression. By 10 seconds, the raccoon is seen swimming near the seabed. The focus then shifts to a group of koi fish swimming gracefully near the seabed from 11 to 13 seconds.

At 14 seconds, the scene transitions to an ukulele resting on the seabed, with the camera zooming in to highlight its details. The raccoon reappears at 20 seconds, swimming among the koi fish, and by 23 seconds, it begins to play the ukulele. The raccoon continues to play the ukulele from 29 to 41 seconds, with the koi fish swimming around it, adding a lively and colorful touch to the underwater setting.

From 42 to 49 seconds, the raccoon is shown playing the ukulele with the koi fish swimming around it. The video concludes with a screen displaying "Video generated with Amazon Novo Reel 1.1" and "Available in Amazon Bedrock" from 50 to 54 seconds, followed by the AWS logo from 55 to 57 seconds.

The TwelveLabs models can be easily integrated into your applications using the Amazon Bedrock Converse API, which provides a unified interface for conversational AI interactions.

Here’s an example of how to use the AWS SDK for Python (Boto3) with the TwelveLabs Pegasus model:

import boto3
import json
import os

AWS_REGION = "us-east-1"
MODEL_ID = "twelvelabs.pegasus-1-2-v1:0"
VIDEO_PATH = "sample.mp4"

def read_file(file_path: str) -> bytes:
    """Read a file in binary mode."""
    try:
        with open(file_path, 'rb') as file:
            return file.read()
    except Exception as e:
        raise Exception(f"Error reading file {file_path}: {str(e)}")

bedrock_runtime = boto3.client(
    service_name="bedrock-runtime",
    region_name=AWS_REGION
)

request_body = {
    "messages": [
        {
            "role": "user",
            "content": [
                {
                    "inputPrompt": "tell me about the video",
                    "mediaSource: {
                        "base64String": read_file(VIDEO_PATH)
                    }
                },
            ],
        }
    ]
}

response = bedrock_runtime.converse(
    modelId=MODEL_ID,
    messages=request_body["messages"]
)

print(response["output"]["message"]["content"][-1]["text"])

The TwelveLabs Marengo Embed 2.7 model generates vector embeddings from video, text, audio, or image inputs. These embeddings can be used for similarity search, clustering, and other machine learning (ML) tasks. The model supports asynchronous inference through the Bedrock AsyncInvokeModel API.

For video source, you can request JSON format for the TwelveLabs Marengo Embed 2.7 model using the AsyncInvokeModel API.

{
    "modelId": "twelvelabs.marengo-embed-2.7",
    "modelInput": {
        "inputType": "video",
        "mediaSource": {
            "s3Location": {
                "uri": "s3://your-video-object-s3-path",
                "bucketOwner": "your-video-object-s3-bucket-owner-account"
            }
        }
    },
    "outputDataConfig": {
        "s3OutputDataConfig": {
            "s3Uri": "s3://your-bucket-name"
        }
    }
}

You can get a response delivered to the specified S3 location.

{
    "embedding": [0.345, -0.678, 0.901, ...],
    "embeddingOption": "visual-text",
    "startSec": 0.0,
    "endSec": 5.0
}

To help you get started, check out a broad range of code examples for multiple use cases and a variety of programming languages. To learn more, visit TwelveLabs Pegasus 1.2 and TwelveLabs Marengo Embed 2.7 in the AWS Documentation.

Now available
TwelveLabs models are generally available today in Amazon Bedrock: the Marengo model in the US East (N. Virginia), Europe (Ireland), and Asia Pacific (Seoul) Region, and the Pegasus model in US West (Oregon), and Europe (Ireland) Region accessible with cross-Region inference from US and Europe Regions. Check the full Region list for future updates. To learn more, visit the TwelveLabs in Amazon Bedrock product page and the Amazon Bedrock pricing page.

Give TwelveLabs models a try on the Amazon Bedrock console today, and send feedback to AWS re:Post for Amazon Bedrock or through your usual AWS Support contacts.

— Channy

New Amazon EC2 P6e-GB200 UltraServers accelerated by NVIDIA Grace Blackwell GPUs for the highest AI performance

Post Syndicated from Channy Yun (윤석찬) original https://aws.amazon.com/blogs/aws/new-amazon-ec2-p6e-gb200-ultraservers-powered-by-nvidia-grace-blackwell-gpus-for-the-highest-ai-performance/

Today, we’re announcing the general availability of Amazon Elastic Compute Cloud (Amazon EC2) P6e-GB200 UltraServers, accelerated by NVIDIA GB200 NVL72 to offer the highest GPU performance for AI training and inference. Amazon EC2 UltraServers connect multiple EC2 instances using a dedicated, high-bandwidth, and low-latency accelerator interconnect across these instances.

The NVIDIA Grace Blackwell Superchips connect two high-performance NVIDIA Blackwell tensor core GPUs and an NVIDIA Grace CPU based on Arm architecture using the NVIDIA NVLink-C2C interconnect. Each Grace Blackwell Superchip delivers 10 petaflops of FP8 compute (without sparsity) and up to 372 GB HBM3e memory. With the superchip architecture, GPU and CPU are colocated within one compute module, increasing bandwidth between GPU and CPU significantly compared to current generation EC2 P5en instances.

With EC2 P6e-GB200 UltraServers, you can access up to 72 NVIDIA Blackwell GPUs within one NVLink domain to use 360 petaflops of FP8 compute (without sparsity) and 13.4 TB of total high bandwidth memory (HBM3e). Powered by the AWS Nitro System, P6e-GB200 UltraServers are deployed in EC2 UltraClusters to securely and reliably scale to tens of thousands of GPUs.

EC2 P6e-GB200 UltraServers deliver up to 28.8 Tbps of total Elastic Fabric Adapter (EFAv4) networking. EFA is also coupled with NVIDIA GPUDirect RDMA to enable low-latency GPU-to-GPU communication between servers with operating system bypass.

EC2 P6e-GB200 UltraServers specifications
EC2 P6e-GB200 UltraServers are available in sizes ranging from 36 to 72 GPUs under NVLink. Here are the specs for EC2 P6e-GB200 UltraServers:

UltraServer type GPUs
 GPU
memory (GB)		 vCPUs Instance memory
(GiB)		 Instance storage (TB) Aggregate EFA Network Bandwidth (Gbps) EBS bandwidth (Gbps)
u-p6e-gb200x36 36 6660 1296 8640 202.5 14400 540
u-p6e-gb200x72 72 13320 2592 17280 405 28800 1080

P6e-GB200 UltraServers are ideal for the most compute and memory intensive AI workloads, such as training and inference of frontier models, including mixture of experts models and reasoning models, at the trillion-parameter scale.

You can build agentic and generative AI applications, including question answering, code generation, video and image generation, speech recognition, and more.

P6e-GB200 UltraServers in action
You can use EC2 P6e-GB200 UltraServers in the Dallas Local Zone through EC2 Capacity Blocks for ML. The Dallas Local Zone (us-east-1-dfw-2a) is an extension of the US East (N. Virginia) Region.

To reserve your EC2 Capacity Blocks, choose Capacity Reservations on the Amazon EC2 console. You can select Purchase Capacity Blocks for ML and then choose your total capacity and specify how long you need the EC2 Capacity Block for u-p6e-gb200x36 or u-p6e-gb200x72 UltraServers.

Once Capacity Block is successfully scheduled, it is charged up front and its price doesn’t change after purchase. The payment will be billed to your account within 12 hours after you purchase the EC2 Capacity Blocks. To learn more, visit Capacity Blocks for ML in the Amazon EC2 User Guide.

To run instances within your purchased Capacity Block, you can use AWS Management Console, AWS Command Line Interface (AWS CLI) or AWS SDKs. On the software side, you can start with the AWS Deep Learning AMIs. These images are preconfigured with the frameworks and tools that you probably already know and use: PyTorch, JAX, and a lot more.

You can also integrate EC2 P6e-GB200 UltraServers seamlessly with various AWS managed services. For example:

  • Amazon SageMaker Hyperpod provides managed, resilient infrastructure that automatically handles the provisioning and management of P6e-GB200 UltraServers, replacing faulty instances with preconfigured spare capacity within the same NVLink domain to maintain performance.
  • Amazon Elastic Kubernetes Services (Amazon EKS) allows one managed node group to span across multiple P6e-GB200 UltraServers as nodes, automating their provisioning and lifecycle management within Kubernetes clusters. You can use EKS topology-aware routing for P6e-GB200 UltraServers, enabling optimal placement of tightly coupled components of distributed workloads within a single UltraServer’s NVLink-connected instances.
  • Amazon FSx for Lustre file systems provide data access for P6e-GB200 UltraServers at the hundreds of GB/s of throughput and millions of input/output operations per second (IOPS) required for large-scale HPC and AI workloads. For fast access to large datasets, you can use up to 405 TB of local NVMe SSD storage or virtually unlimited cost-effective storage with Amazon Simple Storage Service (Amazon S3).

Now available
Amazon EC2 P6e-GB200 UltraServers are available today in the Dallas Local Zone (us-east-1-dfw-2a) through EC2 Capacity Blocks for ML. For more information, visit the Amazon EC2 pricing page.

Give Amazon EC2 P6e-GB200 UltraServers a try in the Amazon EC2 console. To learn more, visit the Amazon EC2 P6e instances page and send feedback to AWS re:Post for EC2 or through your usual AWS Support contacts.

— Channy

Introducing AWS Builder Center: A new home for the AWS builder community

Post Syndicated from Channy Yun (윤석찬) original https://aws.amazon.com/blogs/aws/introducing-aws-builder-center-a-new-home-for-the-aws-builder-community/

We really love builders at AWS. We’re constantly thinking of new ways to help technical communities thrive and create spaces like AWS Developer Center and community.aws where people can connect and share their knowledge and experiences.

Today, we’re announcing AWS Builder Center, a new home for builders to access all builder resources, engage with the AWS community, and provide feedback or product suggestions to AWS product teams. This new experience also integrates the previous AWS Developer Center and community.aws.

There are a variety of exciting features so let us discover some of them.

Your voice matters: Introducing Wishlist
One of the most exciting new features, in my opinion, is Wishlist. You can now submit your wishes for new features or improvements you’d like to see in AWS services. Others can discover and vote on these wishes while also creating their own.

You can influence product roadmap collectively as a community and help us shape the future of AWS services. You can share ideas, suggestions, feature proposals, or challenges while operating AWS services, with the ability for the AWS community to upvote ideas and highlight the most sought-after improvements. Our internal teams will keep an eye on these and bring the most popular wishes to the attention of our service teams, making your voice an integral part of our product development process.

Connect people in the AWS community
On the Connect page, you’ll find many opportunities to connect directly with AWS Heroes and AWS Community Builders. You can explore and join AWS User Groups and AWS Cloud Clubs near your cities around the world.

On top of that, you can bookmark this page as your centralized hub for finding upcoming community events, making it easy to find opportunities to learn and network in your local area and meet like-minded builders who share your interests.

Speaking of following people, AWS Builder Center makes it really straightforward to connect and engage with others, serving as the central hub for the AWS technical community. It brings together all the different ways that you can connect with fellow builders. For example, the Who to Follow section introduces you to AWS Heroes, Community Builders, and active community members who are sharing their knowledge and expertise in your areas of interest.

Explore our AWS hands-on resources
On the Build page, you’ll discover ways to get familiar with AWS with hands-on experience such as interactive learning resources designed for every skill level such as AWS Tutorials and AWS Workshops. You can explore generative AI and agentic AI services playground and find the AWS Free Tier to try out AWS services free of charge up to specified limits for each service.

Choose the Toolbox page and discover the latest tools, programming language resources, and Open Source projects for AWS. The Toolbox has everything you need to get your project scaffolded and up and running.

To improve the build experience for builders, we plan to expand Builder Center’s built-in offerings such as creating dedicated groups and forums for collaborating on a particular topic, run workshops for hands-on labs, and various service playgrounds where builders can freely experiment with AWS services.

Supporting your builder journey
The new Learn section serves as your gateway to skill development, bringing together everything you need to expand your AWS expertise. Here, you can explore learning and training resources, workshops, gamified experiences, and more to make your journey of building on AWS both educational and engaging.

Choose the Topics page, where you can explore and discover more content. You can explore content by topics and tags. There is a featured and trending topics section that helps you to stay connected with what’s capturing the community’s attention right now.

Built-in localization for your spoken language
AWS Builder Center breaks down language barriers with comprehensive localization support. All content published in the Builder Center is automatically available in 16 languages, and user-generated content, such as posts, comments, or wishes, can be machine-translated on demand using Translate. So, you can collaborate with builders worldwide, sharing knowledge and experiences across language boundaries.

By default, all content will be displayed in based on the language that your browser is set to. But, you can override this by visiting the settings page and choosing the language that you want AWS Builder Center to use by default.

Sign up and build your profile now
AWS Builder Center gives you a more personalized and comprehensive way to showcase your AWS journey. Your unique profile comes with a custom URL and shareable QR code, making it straightforward to connect with others and share your presence in the AWS community.

All your posts, wishes, and meaningful interactions are organized within a centralized view so you can easily check them. In the Manage profile page, you can customize your profile, add specific interests and areas of expertise, helping you connect with builders who share your passions. Profile management is seamless: it synchronizes across all AWS services using AWS Builder ID, ensuring your identity remains consistent wherever you engage with AWS offerings.

Visit builder.aws.com, sign up with AWS Builder ID, and claim your unique alias to access all features, including content creation, Wishlist, and community engagement tools.

AWS Builder Center was designed to help you connect, learn, and build with fellow AWS builders, so enjoy your journey together!

— Channy & Matheus Guimaraes | @codingmatheus

Introducing Oracle Database@AWS for simplified Oracle Exadata migrations to the AWS Cloud

Post Syndicated from Channy Yun (윤석찬) original https://aws.amazon.com/blogs/aws/introducing-oracle-databaseaws-for-simplified-oracle-exadata-migrations-to-the-aws-cloud/

Today, we’re announcing the general availability of Oracle Database@AWS, a new offering for Oracle Exadata workloads, including Oracle Real Application Clusters (RAC) within AWS.

In the past 14 years, customers had the choice of self-managing Oracle database workloads in the cloud using Amazon Elastic Compute Cloud (Amazon EC2) or using fully managed Amazon Relational Database Service (Amazon RDS) for Oracle. Now, you have an additional option for your workloads that require Oracle RAC or Oracle Exadata for quicker and simpler migrations to the cloud. You also get a single invoice through AWS Marketplace, which counts towards AWS commitments and Oracle license benefits, including Bring Your Own License (BYOL) and discount programs such as Oracle Support Rewards.

With Oracle Database@AWS, you can migrate your Oracle Exadata workloads to Oracle Exadata Database Service on Dedicated Infrastructure or Oracle Autonomous Database on Dedicated Exadata Infrastructure within AWS with minimal changes. You can purchase, provision, and manage your Oracle Database@AWS deployments through familiar AWS tools and interfaces such as AWS Management Console, AWS Command Line Interface (AWS CLI), or AWS APIs for applications running on AWS. The AWS APIs call the corresponding Oracle Cloud Infrastructure (OCI) APIs necessary to provision and manage the resources.

Since its preview last December, we’ve improved or added features to help run production workloads at general availability:

  • Regional expansion – You can now use Oracle Database@AWS in the U.S. East (N. Virginia) and U.S. West (Oregon) Regions today. We are also announcing plans to expand to 20 AWS Regions globally. This broader availability supports the diverse needs of our customers across various geographical areas so more enterprises can benefit from this option. You can choose from different Exadata system sizes to match your workload requirements in your AWS Region.
  • Zero-ETL and S3 backups – You can now benefit from zero-ETL integration with Amazon Redshift for analytics to remove the need to build and manage data pipelines for extract, transform, and load operations. With zero-ETL, you can unify your data on AWS without incurring cross network data transfer costs. We’re providing Amazon Simple Storage Service (Amazon S3) backups with up to eleven nines of data durability.
  • Autonomous VM cluster – You can now provision an Autonomous VM Cluster in addition to an Exadata VM cluster on the Exadata Dedicated Infrastructure. You can run Oracle Autonomous Database on Dedicated Exadata Infrastructure, a fully managed database environment using committed hardware and software resources.

Oracle Database@AWS also integrates with other AWS services such as Amazon Virtual Private Cloud (Amazon VPC) Lattice for configuring network paths to AWS services such as S3 and Redshift directly, AWS Identity and Access Management (IAM) for authentication and authorization, Amazon EventBridge for monitoring database lifecycle events, AWS CloudFormation for infrastructure automation, Amazon CloudWatch for collecting and monitoring metrics, and AWS CloudTrail for logging API operations.

Getting started with Oracle Database@AWS
Oracle Database@AWS supports two key services: Oracle Exadata Database Service on Dedicated Infrastructure and Oracle Autonomous Database on Dedicated Exadata Infrastructure within AWS data centers.

These services physically reside within an Availability Zone in an AWS Region and logically reside in an OCI region, enabling seamless integration with AWS services through high-speed, low-latency connections.

You create an ODB network, a private, isolated network that hosts Oracle Exadata VM Clusters within an Availability Zone. Then, you use ODB peering accessible to EC2 application servers running in a VPC. To learn more, visit How Oracle Database@AWS works in the AWS documentation.

Request a private offer in AWS Marketplace

To begin your journey with Oracle Database@AWS, visit the AWS console or request the AWS Marketplace private offer. Your AWS and Oracle sales team will receive your request, then contact you to find the best option for your workloads, and activate your account.

When you activate and get access to Oracle Database@AWS, you can use the Dashboard to create an ODB network, Exadata infrastructure, and Exadata VM cluster or Autonomous VM cluster, and ODB peering connection.

To learn more, visit the Onboarding to Oracle Database@AWS and AWS Marketplace buyer private offers in the AWS documentation.

Create an ODB network

An ODB network is a private isolated network that hosts OCI infrastructure on AWS. The ODB network maps directly to the network that exists within the OCI child site, thus serving as the means of communication between AWS and OCI.

In the Dashboard, choose Create ODB network, enter a network name, choose the Availability Zone, and specify a CIDR ranges for client connections established by applications and backup connections used for taking automated backups. You can also enter a name to use as a prefix to your domain fixed as oraclevcn.com. For example, if you enter myhost, the fully qualified domain name is myhost.oraclevcn.com.

Optionally, you can configure ODB network access to perform automated backups to Amazon S3 and zero-ETL for near real-time analytics and ML on your Oracle data using Amazon Redshift.

After you create your ODB network, update your VPC route tables of your EC2 application servers with the client connection CIDR in the ODB network. To learn more, visit ODB network, ODB peering, and Configuring VPC route tables for ODB peering in the AWS documentation.

Create Exadata infrastructure

The Oracle Exadata infrastructure is the underlying architecture of your database servers, storage servers, and networking that run your Oracle Exadata databases.

Choose Create Exadata infrastructure, enter a name, and use the default Availability Zone. In the next step, you can choose Exadata.X11M for the Exadata system model. You can also set a default of 2 or up to 32 database servers and 3 or up to 64 storage servers with 80 TB storage capacity per server.

Finally, you can configure system maintenance preferences, such as scheduling, patching mode, and OCI maintenance notification contacts. You can’t modify an infrastructure after you create it from the AWS console. But, you can navigate to the OCI console and modify it.

To delete an Exadata infrastructure, visit Deleting an Oracle Exadata infrastructure in Oracle Database@AWS in the AWS documentation.

Create an Exadata VM cluster or Autonomous VM cluster

You can create VM clusters on Exadata infrastructure and deploy multiple VM clusters with different Oracle Exadata infrastructures in the same ODB network.

Here are two types of VM clusters:

  • An Exadata VM cluster is a set of virtual machines that has a complete Oracle database installation that includes all features of Oracle Enterprise Edition.
  • An Autonomous VM cluster is a set of fully managed databases that automate key management tasks using AI/ML with no human intervention required.

Choose Create Exadata VM cluster, enter a VM cluster name and a time zone, choose Bring Your Own License (BYOL) or license included for license options. In the next step, you can choose your Exadata infrastructure, grid infrastructure version, and Exadata image version. For database servers, you can choose the CPU core count, memory, and local storage for each VM or accept the defaults.

In the next step, you can configure the connectivity setting by choosing your ODB network and entering a prefix for the VM cluster. You can enter a port number for TCP access to the single client access name (SCAN) listener. The default port is 1521 or you can enter a custom SCAN port in the range 1024–8999. For SSH key pairs, enter the public key portion of one or more key pairs used for SSH access to the VM cluster.

Then, you can choose diagnostics and tags, review your settings, and create a VM cluster. The creation process can take up to 6 hours, depending on the size of the VM cluster.

Create and manage an Oracle database

When the VM cluster is ready, you can create and manage your Oracle Exadata databases in the OCI console. Choose Manage in OCI in the details page of the Exadata VM cluster. You will be redirected to the OCI console.

When you create an Oracle Database in the OCI console, you can select Oracle Database 19c or 23ai. When enabling automatic backups for your provisioned databases, you can use an S3 bucket or OCI Object Storage in the OCI region. To learn more, visit Provision Oracle Exadata Database Service in Oracle Database@AWS in the OCI documentation.

Things to know
Here are a couple of things to know about Oracle Database@AWS:

  • Monitoring – You can monitor Oracle Database@AWS using Amazon CloudWatch metrics in the AWS/ODB namespaces for VM clusters, container databases, and pluggable databases. AWS CloudTrail captures all AWS API calls for Oracle Database@AWS as events. Using CloudTrail logs, you can determine the request that was made to Oracle Database@AWS, the IP address from which the request was made, when it was made, and additional details. To learn more, visit Monitoring Oracle Database@AWS.
  • Security – You can use IAM to assign permissions that determine who is allowed to manage Oracle Database@AWS resources and SSL/TLS encrypted connections to secure data. You can also use Amazon EventBridge for seamless event-driven database operations—all working together to maintain security standards while enabling efficient cloud operations. To learn more, visit Security in Oracle Database@AWS.
  • Compliance – Your compliance responsibility when using Oracle Database@AWS is determined by the sensitivity of your data, your company’s compliance objectives, and applicable laws and regulations. We provides the following compliances with Oracle Database@AWS: SOC 1, SOC 2, SOC 3, HIPAA, C5, CSA STAR Attest, CSA STAR Cert, HDS (France), ISO Series (ISO/IEC 9001, 20000-1, 27001, 27017, 27018, 27701, 22301), PCI DSS, and HITRUST. To learn more, visit Compliance validation for Oracle Database@AWS.
  • Support – Your AWS or Oracle sales account team can help you evaluate your current database infrastructure, determine how Oracle Database@AWS can best serve your organization’s requirements, and develop a tailored migration strategy and timeline. You can also get help from AWS Oracle Competency Partners specialized to architect, deploy, and manage Oracle-based workloads running in the AWS Cloud.

Now available and coming soon
Oracle Database@AWS is now available in the U.S. East (N. Virginia) and U.S. West (Oregon) Regions through the AWS Marketplace. Oracle Database@AWS pricing and any AWS Marketplace private offers are set by Oracle. You can see specific details around pricing on Oracle’s pricing page for the offering.

Oracle Database@AWS will expand to 20 more AWS Regions across the Americas, Europe, and Asia-Pacific including: US East (Ohio), US West (N. California), Asia Pacific (Hyderabad), Asia Pacific (Melbourne), Asia Pacific (Mumbai), Asia Pacific (Osaka), Asia Pacific (Seoul), Asia Pacific (Singapore), Asia Pacific (Sydney), Asia Pacific (Tokyo), Canada (Central), Europe (Frankfurt), Europe (Ireland), Europe (London), Europe (Milan), Europe (Paris), Europe (Spain), Europe (Stockholm), Europe (Zurich), and South America (São Paulo).

You can get started with Oracle Database@AWS with using AWS console. To learn more, visit the Oracle Database@AWS User Guide and OCI documentation and send feedback through your usual AWS Support contacts or OCI support.

— Channy

New Amazon EC2 C8gn instances powered by AWS Graviton4 offering up to 600Gbps network bandwidth

Post Syndicated from Channy Yun (윤석찬) original https://aws.amazon.com/blogs/aws/new-amazon-ec2-c8gn-instances-powered-by-aws-graviton4-offering-up-to-600gbps-network-bandwidth/

Today, we’re announcing the general availability of Amazon Elastic Compute Cloud (Amazon EC2) C8gn network optimized instances powered by AWS Graviton4 processors and the latest 6th generation AWS Nitro Card. EC2 C8gn instances deliver up to 600Gbps network bandwidth, the highest bandwidth among EC2 network optimized instances.

You can use C8gn instances to run the most demanding network intensive workloads, such as security and network virtual appliances (virtual firewalls, routers, load balancers, proxy servers, DDoS appliances), data analytics, and tightly-coupled cluster computing jobs.

EC2 C8gn instances specifications
C8gn instances provide up to 192 vCPUs and 384 GiB memory, and offer up to 30 percent higher compute performance compared Graviton3-based EC2 C7gn instances.

Here are the specs for C8gn instances:

Instance Name vCPUs Memory (GiB) Network Bandwidth (Gbps) EBS Bandwidth (Gbps)
c8gn.medium 1 2 Up to 25 Up to 10
c8gn.large 2 4 Up to 30 Up to 10
c8gn.xlarge 4 8 Up to 40 Up to 10
c8gn.2xlarge 8 16 Up to 50 Up to 10
c8gn.4xlarge 16 32 50 10
c8gn.8xlarge 32 64 100 20
c8gn.12xlarge 48 96 150 30
c8gn.16xlarge 64 128 200 40
c8gn.24xlarge 96 192 300 60
c8gn.metal-24xl 96 192 300 60
c8gn.48xlarge 192 384 600 60
c8gn.metal-48xl 192 384 600 60

You can launch C8gn instances through the AWS Management Console, AWS Command Line Interface (AWS CLI), or AWS SDKs.

If you’re using C7gn instances now, you will have straightforward experience migrating network intensive workloads to C8gn instances because the new instances offer similar vCPU and memory ratios. To learn more, check out the collection of Graviton resources to help you start migrating your applications to Graviton instance types.

You can also visit the Level up your compute with AWS Graviton page to begin your Graviton adoption journey.

Now available
Amazon EC2 C8gn instances are available today in US East (N. Virginia) and US West (Oregon) Regions. Two metal instance sizes are only available in US East (N. Virginia) Region. These instances can be purchased as On-Demand, Savings Plan, Spot instances, or as Dedicated instances and Dedicated hosts.

Give C8gn instances a try in the Amazon EC2 console. To learn more, refer to the Amazon EC2 C8g instance page and send feedback to AWS re:Post for EC2 or through your usual AWS Support contacts.

— Channy

AWS Weekly Roundup: Project Rainier, Amazon CloudWatch investigations, AWS MCP servers, and more (June 30, 2025)

Post Syndicated from Channy Yun (윤석찬) original https://aws.amazon.com/blogs/aws/aws-weekly-roundup-project-rainier-amazon-cloudwatch-investigations-aws-mcp-servers-and-more-june-30-2025/

Every time I visit Seattle, the first thing that greets me at the airport is Mount Rainier. Did you know that the most innovative project at Amazon Web Services (AWS) is named after this mountain?

Project Rainier is a new project to create what is expected to be the world’s most powerful computer for training AI models across multiple data centers in the United Stages. Anthropic will develop the advanced versions of its Claude models with five times more computing power than its current largest training cluster.

The key technology powering Project Rainier is AWS custom-designed Trainium2 chips, which are specialized for the immense data processing required to train complex AI models. Thousands of these Trainium2 chips will be connected in a new type of Amazon EC2 UltraServer and EC2 UltraCluster architecture that allows ultra-fast communication and data sharing across the massive system.

Learn about the AWS vertical integration of Project Rainer, where it designs every component of the technology stack from chips to software, allows it to optimize the entire system for maximum efficiency and reliability.

Last week’s launches
Here are some launches that got my attention:

  • Amazon S3 access for Amazon FSx for OpenZFS – You can access and analyze your FSx for OpenZFS file data through Amazon S3 Access Points, enabling seamless integration with AWS AI/ML, and analytics services without moving your data out of the file system. You can treat your FSx for OpenZFS data as if it were stored in S3, making it accessible through the S3 API for various applications including Amazon Bedrock, Amazon SageMaker, AWS Glue, and other S3 based cloud-native applications.
  • Amazon S3 with sort and z-order compaction for Apache Iceberg tables – You can optimize query performance and reduce costs with new sort and z-order compaction. With S3 Tables, sort compaction automatically organizes data files based on defined column orders, while z-order compaction can be enabled through the maintenance API for efficient multicolumn queries.
  • Amazon CloudWatch investigations – You can accelerate your operational troubleshooting in AWS environments using the Amazon CloudWatch AI-powered investigation feature, which helps identify anomalies, surface related signals, and suggest remediation steps. This capability can be initiated through CloudWatch data widgets, multiple AWS consoles, CloudWatch alarm actions, or Amazon Q chat and enables team collaboration and integration with Slack and Microsoft Teams.
  • Amazon Bedrock Guardrails Standard tier – You can enhance your AI content safety measures using the new Standard tier. It offers improved content filtering and topic denial capabilities across up to 60 languages, better detection of variations including typos, and stronger protection against prompt attacks. This feature lets you configure safeguards to block harmful content, prevent model hallucinations, redact personally identifiable information (PII), and verify factual claims through automated reasoning checks.
  • Amazon Route 53 Resolver endpoints for private hosted zone – You can simplify DNS management across AWS and on-premises infrastructure using the new Route 53 DNS delegation feature for private hosted zone subdomains, which works with both inbound and outbound Resolver endpoints. You can delegate subdomain authority between your on-premises infrastructure and Route 53 Resolver cloud service using name server records, eliminating the need for complex conditional forwarding rules.
  • Amazon Q Developer CLI for Java transformation – You can automate and scale Java application upgrades using the new Amazon Q Developer Java transformation command line interface (CLI). This feature perform upgrades from Java versions 8, 11, 17, or 21 to versions 17 or 21 directly from the command line. This tool offers selective transformation options so you can choose specific steps from transformation plans and customize library upgrades.
  • New AWS IoT Device Management managed integrations – You can simplify Internet of Things (IoT) device management across multiple manufacturers and protocols using the new managed integrations feature, which provides a unified interface for controlling devices whether they connect directly, through hubs or third-party clouds. The feature includes pre-built cloud-to-cloud (C2C) connectors, device data model templates, and SDKs that support ZigBee, Z-Wave, and Wi-Fi protocols, while you can still create custom connectors and data models.

For a full list of AWS announcements, be sure to keep an eye on the What’s New with AWS? page.

Other AWS news
Various Model Context Protocol (MCP) servers for AWS services have been released. Here are some tutorials about MCP servers that you might find interesting:

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

  • AWS re:Invent – Register now to get a head start on choosing your best learning path, booking travel and accommodations, and bringing your team to learn, connect, and have fun. If you’re an early-career professional, you can apply to the All Builders Welcome Grant program, which is designed to remove financial barriers and create diverse pathways into cloud technology.
  • AWS NY Summits – You can gain insights from Swami’s keynote featuring the latest cutting-edge AWS technologies in compute, storage, and generative AI. My News Blog team is also preparing some exciting news for you. If you’re unable to attend in person, you can still participate by registering for the global live stream. Also, save the date for these upcoming Summits in July and August near your city.
  • AWS Builders Online Series – If you’re based in one of the Asia Pacific time zones, join and learn fundamental AWS concepts, architectural best practices, and hands-on demonstrations to help you build, migrate, and deploy your workloads on AWS.

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

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

— Channy

AWS Certificate Manager introduces exportable public SSL/TLS certificates to use anywhere

Post Syndicated from Channy Yun (윤석찬) original https://aws.amazon.com/blogs/aws/aws-certificate-manager-introduces-exportable-public-ssl-tls-certificates-to-use-anywhere/

Today, we’re announcing exportable public SSL/TLS certificates from AWS Certificate Manager (ACM). Prior to this launch, you can issue your public certificates or import certificates issued by third-party certificate authorities (CAs) at no additional cost, and deploy them with integrated AWS services such as Elastic Load Balancing (ELB), Amazon CloudFront distribution, and Amazon API Gateway.

Now you can export public certificates from ACM, get access to the private keys, and use them on any workloads running on Amazon Elastic Compute Cloud (Amazon EC2) instances, containers, or on-premises hosts. The exportable public certificate are valid for 395 days. There is a charge at time of issuance, and again at time of renewal. Public certificates exported from ACM are issued by Amazon Trust Services and are widely trusted by commonly used platforms such as Apple and Microsoft and popular web browsers such as Google Chrome and Mozilla Firefox.

ACM exportable public certificates in action
To export a public certificate, you first request a new exportable public certificate. You cannot export previously created public certificates.

To get started, choose Request certificate in the ACM console and choose Enable export in the Allow export section. If you select Disable export, the private key for this certificate will be disallowed for exporting from ACM and this cannot be changed after certificate issuance.

You can also use the request-certificate command to request a public exportable certificate with Export=ENABLED option on the AWS Command Line Interface (AWS CLI).

aws acm request-certificate \
--domain-name mydomain.com \
--key-algorithm EC_Prime256v1 \
--validation-method DNS \
--idempotency-token <token> \
--options \
CertificateTransparencyLoggingPreference=DISABLED \
Export=ENABLED

After you request the public certificate, you must validate your domain name to prove that you own or control the domain for which you are requesting the certificate. The certificate is typically issued within seconds after successful domain validation.

When the certificate enters status Issued, you can export your issued public certificate by choosing Export.

Export your public certificate

Enter a passphrase for encrypting the private key. You will need the passphrase later to decrypt the private key. To get the public key, Choose Generate PEM Encoding.

You can copy the PEM encoded certificate, certificate chain, and private key or download each to a separate file.

Download PEM keys

You can use the export-certificate command to export a public certificate and private key. For added security, use a file editor to store your passphrase and output keys to a file to prevent being stored in the command history.

aws acm export-certificate \
     --certificate-arn arn:aws:acm:us-east-1:<accountID>:certificate/<certificateID> \
     --passphrase fileb://path-to-passphrase-file \
     | jq -r '"\(.Certificate)\(.CertificateChain)\(.PrivateKey)"' \
     > /tmp/export.txt

You can now use the exported public certificates for any workload that requires SSL/TLS communication such as Amazon EC2 instances. To learn more, visit Configure SSL/TLS on Amazon Linux in your EC2 instances.

Things to know
Here are a couple of things to know about exportable public certificates:

  • Key security – An administrator of your organization can set AWS IAM policies to authorize roles and users who can request exportable public certificates. ACM users who have current rights to issue a certificate will automatically get rights to issue an exportable certificate. ACM admins can also manage the certificates and take actions such as revoking or deleting the certificates. You should protect exported private keys using secure storage and access controls.
  • Revocation – You may need to revoke exportable public certificates to comply with your organization’s policies or mitigate key compromise. You can only revoke the certificates that were previously exported. The certificate revocation process is global and permanent. Once revoked, you can’t retrieve revoked certificates to reuse. To learn more, visit Revoke a public certificate in the AWS documentation.
  • Renewal – You can configure automatic renewal events for exportable public certificates by Amazon EventBridge to monitor certificate renewals and create automation to handle certificate deployment when renewals occur. To learn more, visit Using Amazon EventBridge in the AWS documentation. You can also renew these certificates on-demand. When you renew the certificates, you’re charged for a new certificate issuance. To learn more, visit Force certificate renewal in the AWS documentation.

Now available
You can now issue exportable public certificates from ACM and export the certificate with the private keys to use other compute workloads as well as ELB, Amazon CloudFront, and Amazon API Gateway.

You are subject to additional charges for an exportable public certificate when you create it with ACM. It costs $15 per fully qualified domain name and $149 per wildcard domain name. You only pay once during the lifetime of the certificate and will be charged again only when the certificate renews. To learn more, visit the AWS Certificate Manager Service Pricing page.

Give ACM exportable public certificates a try in the ACM console. To learn more, visit the ACM Documentation page and send feedback to AWS re:Post for ACM or through your usual AWS Support contacts.

— Channy

Introducing AWS API models and publicly available resources for AWS API definitions

Post Syndicated from Channy Yun (윤석찬) original https://aws.amazon.com/blogs/aws/introducing-aws-api-models-and-publicly-available-resources-for-aws-api-definitions/

Today, we’re announcing a new publicly available source of API models for Amazon Web Services (AWS). We are now publishing AWS API models on a daily basis to Maven Central and providing open source access to a new repository on GitHub. This repository includes a definitive, up-to-date source of Smithy API models that define AWS public interface definitions and behaviors.

These Smithy models can be used to better understand AWS services and build developer tools like custom software development kits (SDK) and command line interfaces (CLIs) for connecting to AWS or testing tools for validating your application integrations on AWS.

Since 2018, we have been generating SDK clients and CLI tools using Smithy models. All AWS services are modeled in Smithy to thoroughly document the API contract including operations and behaviors like protocols, authentication, request and response types, and errors.

With this public resource, you can build and test your own applications that can integrate directly with AWS services with confidence such as:

  • Generate SDK clients – You can build your own, purpose-built SDKs for language communities without official AWS SDK support and client code generator using Smithy toolchain to generate client SDK libraries.
  • Generating API implementations – You can generate server stubs for language-specific framework, even model context protocol (MCP) server configurations for your AI agents. You have built-in validation to ensure you adhere to your own API standards.
  • Build your own developer tools – You can build your own tools on top of AWS such as mock testing tools, IAM policy generators, or higher-level abstractions for connecting to AWS.
  • Understand AWS API behaviors – You can concisely and easily investigate your artifact to quickly review and understand how SDKs interpret API calls and the behaviors to expect with those calls.

Learn about AWS API models
You can browse the AWS service models directly on GitHub by accessing the api-models-aws repository. This repository contains Smithy models with the JSON AST format for all public AWS API services. All Smithy models consist of shapes and traits. Shapes are instances of types and traits are used to add more information to shapes that might be useful for clients, servers, or documentation.

The AWS models repository contains:

  • Top-level service directories are named using the <sdk-id> of the service, where <sdk-id> is the value of the model’s sdkId, lowercased and with spaces converted to hyphens
  • Each service directory contains one directory per <version> of the service, where <version> is the value of the service shape’s version property.
  • Contained within a service-version directory, a model file named <sdk-id>-<version>.json will be present

For example, when you want to define a RunInstances API in Amazon EC2 service, the model uses service type, an entry point of an API that aggregates resources and operations together. The shape referenced by a member is called its target.

com.amazonaws.ec2#AmazonEC2": {
      "type": "service",
      "version": "2016-11-15",
      "operations": [
....
        {
          "target": "com.amazonaws.ec2#RunInstances"
        },
....
	  ]

The operation type represents the input, output, traits, and possible errors of an API operation. Operation shapes are bound to resource shapes and service shapes. An operation is defined in the IDL using an operation_statement. In the traits, you can find detailed API information such as documentation, examples, and so on.

"com.amazonaws.ec2#RunInstances": {
      "type": "operation",
      "input": {
        "target": "com.amazonaws.ec2#RunInstancesRequest"
      },
      "output": {
        "target": "com.amazonaws.ec2#Reservation"
      },
      "traits": {
        "smithy.api#documentation": "<p>Launches the specified number of instances using an AMI for which you have....",
        smithy.api#examples": [
          {
            "title": "To launch an instance",
            "documentation": "This example launches an instance using the specified AMI, instance type, security group, subnet, block device mapping, and tags.",
            "input": {
              "BlockDeviceMappings": [
                {
                  "DeviceName": "/dev/sdh",
                  "Ebs": {
                    "VolumeSize": 100
                  }
                }
              ],
              "ImageId": "ami-abc12345",
              "InstanceType": "t2.micro",
              "KeyName": "my-key-pair",
              "MaxCount": 1,
              "MinCount": 1,
              "SecurityGroupIds": [
                "sg-1a2b3c4d"
              ],
              "SubnetId": "subnet-6e7f829e",
              "TagSpecifications": [
                {
                  "ResourceType": "instance",
                  "Tags": [
                    {
                      "Key": "Purpose",
                      "Value": "test"
                    }
                  ]
                }
              ]
            },
            "output": {}
          }
        ]
      }
    },

We use Smithy extensively to model our service APIs and provide the daily releases of the AWS SDKs and AWS CLI. AWS API models can be helpful for implementing server stubs to interact with AWS services.

How to build with AWS API models
Smithy API models provide building resources such as build tools, client or server code generators, IDE support, and implementations. For example, with Smithy CLI, you can easily build your models, run ad-hoc validation, compare models for differences, query models, and more. The Smithy CLI makes it easy to get started working with Smithy without setting up Java or using the Smithy Gradle Plugins.

I want to show two examples how to build your own applications with AWS API models and Smithy build tools.

  • Build a minimal SDK client – This sample project provides a template to get started using Smithy TypeScript to create a minimal AWS SDK client for Amazon DynamoDB. You can build the minimal SDK from the Smithy model, and then run the example code. To learn more, visit the example project here.
  • Build MCP servers – This sample project provides a template to generate a fat jar which contains all the dependencies required to run a MCP StdIO server using the Smithy CLI. You can find MCPServerExample to build an MCP server by modeling tools as Smithy APIs and ProxyMCPExample to create a proxy MCP Server for any Smithy service. To learn more, visit the GitHub repository.

Now available
You can now access AWS API models on a daily basis providing open-source access on the AWS API models repository and service model packages available on Maven Central. You can import models and add dependencies using the maven package of their choice.

To learn more about the AWS preferred API modeling language, visit Smithy.io and its code generation guide. To learn more each AWS SDKs, visit Tools to Build on AWS and its respective repository for SDK specific support or through your usual AWS Support contacts.

— Channy

Announcing up to 45% price reduction for Amazon EC2 NVIDIA GPU-accelerated instances

Post Syndicated from Channy Yun (윤석찬) original https://aws.amazon.com/blogs/aws/announcing-up-to-45-price-reduction-for-amazon-ec2-nvidia-gpu-accelerated-instances/

Customers across industries are harnessing the power of generative AI on AWS to boost employee productivity, deliver exceptional customer experiences, and streamline business processes. However, the growth in demand for GPU capacity has outpaced industry-wide supply, making GPUs a scarce resource and increasing the cost of securing them.

As Amazon Web Services (AWS) grows, we work hard to lower our costs so that we can pass those savings back to our customers. Regular price reductions on AWS services have been a standard way for AWS to pass on the economic efficiencies gained from our scale back to our customers.

Today, we’re announcing up to 45 percent price reduction for Amazon Elastic Compute Cloud (Amazon EC2) NVIDIA GPU-accelerated instances: P4 (P4d and P4de) and P5 (P5 and P5en) instance types. This price reduction to On-Demand and Savings Plan pricing applies to all Regions where these instances are available. The pricing reduction applies to On-Demand purchases beginning June 1 and to Savings Plan purchases effective after June 4.

Here is a table of price reductions percentage (%) from May 31, 2025 baseline prices by instance types and pricing plans:

Instance type NVIDIA GPUs On-Demand EC2 Instance Savings Plans Compute Savings Plans
1 year 3 years 1 year 3 years
P4d A100 33% 31% 25% 31% –
P4de A100 33% 31% 25% 31% –
P5 H100 44% – 45% 44% 25%
P5en H200 25% – 26% 25% –

Savings Plans are a flexible pricing model that offer low prices on compute usage, in exchange for a commitment to a consistent amount of usage (measured in $/hour) for a 1- or 3- year term. We offers two types of Savings Plans:

  • EC2 Instance Savings Plans provide the lowest prices, offering savings in exchange for commitment to usage of individual instance families in a Region (for example, P5 usage in the US (N. Virginia) Region).
  • Compute Savings Plans provide the most flexibility and help to reduce your costs regardless of instance family, size, Availability Zones, and Regions (for example, from P4d to P5en instances, shift a workload between US Regions).

To provide increased accessibility to reduced pricing, we are making at-scale On-Demand capacity available for:

  • P4d instances in the Asia Pacific (Seoul), Asia Pacific (Sydney), Canada (Central), and Europe (London) Regions
  • P4de instances in the US East (N. Virginia) Region
  • P5 instances in the Asia Pacific (Mumbai), Asia Pacific (Tokyo), Asia Pacific (Jakarta), and South America (São Paulo) Regions
  • P5en instances in the Asia Pacific (Mumbai), Asia Pacific (Tokyo), and Asia Pacific (Jakarta) Regions

We are also now delivering Amazon EC2 P6-B200 instances through Savings Plan to support large scale deployments, which became available on May 15, 2025 at launch only through EC2 Capacity Blocks for ML. EC2 P6-B200 instances, powered by NVIDIA Blackwell GPUs, accelerate a broad range of GPU-enabled workloads but are especially well-suited for large-scale distributed AI training and inferencing.

These pricing updates reflect the AWS commitment to making advanced GPU computing more accessible while passing cost savings directly to customers.

Give Amazon EC2 NVIDIA GPU-accelerated instances a try in the Amazon EC2 console. To learn more about these pricing updates, visit Amazon EC2 Pricing page and send feedback to AWS re:Post for EC2 or through your usual AWS Support contacts.

— Channy

Amazon Aurora DSQL is now generally available

Post Syndicated from Channy Yun (윤석찬) original https://aws.amazon.com/blogs/aws/amazon-aurora-dsql-is-now-generally-available/

Today, we’re announcing the general availability of Amazon Aurora DSQL, the fastest serverless distributed SQL database with virtually unlimited scale, the highest availability, and zero infrastructure management for always available applications. You can remove the operational burden of patching, upgrades, and maintenance downtime and count on an easy-to-use developer experience to create a new database in a few quick steps.

When we introduced the preview of Aurora DSQL at AWS re:Invent 2024, our customers were excited by this innovative solution to simplify complex relational database challenges. In his keynote, Dr. Werner Vogels, CTO of Amazon.com, talked about managing complexity upfront in the design of Aurora DSQL. Unlike most traditional databases, Aurora DSQL is disaggregated into multiple independent components such as a query processor, adjudicator, journal, and crossbar.

These components have high cohesion, communicate through well-specified APIs, and scale independently based on your workloads. This architecture enables multi-Region strong consistency with low latency and globally synchronized time. To learn more about how Aurora DSQL works behind the scenes, watch Dr. Werner Vogels’ keynote and read about an Aurora DSQL story.

The architecture of Amazon Aurora DSQL
Your application can use the fastest distributed SQL reads and writes and scale to meet any workload demand without any database sharding or instance upgrades. With Aurora DSQL, its active-active distributed architecture is designed for 99.99 percent availability in a single Region and 99.999 percent availability across multiple Regions. This means your applications can continue to read and write with strong consistency, even in the rare case an application is unable to connect to a Region cluster endpoint.

In a single-Region configuration, Aurora DSQL commits all write transactions to a distributed transaction log and synchronously replicates all committed log data to user storage replicas in three Availability Zones. Cluster storage replicas are distributed across a storage fleet and automatically scale to ensure optimal read performance.

Multi-Region clusters provide the same resilience and connectivity as single-Region clusters while improving availability through two Regional endpoints, one for each peered cluster Region. Both endpoints of a peered cluster present a single logical database and support concurrent read and write operations with strong data consistency. A third Region acts as a log-only witness which means there is is no cluster resource or endpoint. This means you can balance applications and connections for geographic locations, performance, or resiliency purposes, making sure readers consistently see the same data.

Aurora DSQL is an ideal choice to support applications using microservices and event-driven architectures, and you can design highly scalable solutions for industries such as banking, ecommerce, travel, and retail. It’s also ideal for multi-tenant software as a service (SaaS) applications and data-driven services like payment processing, gaming platforms, and social media applications that require multi-Region scalability and resilience.

Getting started with Amazon Aurora DSQL
Aurora DSQL provides a easy-to-use experience, starting with a simple console experience. You can use familiar SQL clients to leverage existing skillsets, and integration with other AWS services to improve managing databases.

To create an Aurora DSQL cluster, go to the Aurora DSQL console and choose Create cluster. You can choose either Single-Region or Multi-Region configuration options to help you establish the right database infrastructure for your needs.

1. Create a single-Region cluster

To create a single-Region cluster, you only choose Create cluster. That’s all.

In a few minutes, you’ll see your Aurora DSQL cluster created. To connect your cluster, you can use your favorite SQL client such as PostgreSQL interactive terminal,  DBeaver, JetBrains DataGrip, or you can take various programmable approaches with a database endpoint and authentication token as a password. You can integrate with AWS Secrets Manager for automated token generation and rotation to secure and simplify managing credentials across your infrastructure.

To get the authentication token, choose Connect and Get Token in your cluster detail page. Copy the endpoint from Endpoint (Host) and the generated authentication token after Connect as admin is chosen in the Authentication token (Password) section.

Then, choose Open in CloudShell, and with a few clicks, you can seamlessly connect to your cluster.

After you connect the Aurora DSQL cluster, test your cluster by running sample SQL statements. You can also query SQL statements for your applications using your favorite programming languages: Python, Java, JavaScript, C++, Ruby, .NET, Rust, and Golang. You can build sample applications using a Django, Ruby on Rails, and AWS Lambda application to interact with Amazon Aurora DSQL.

2. Create a multi-Region cluster

To create a multi-Region cluster, you need to add the other cluster’s Amazon Resource Name (ARN) to peer the clusters.

To create the first cluster, choose Multi-Region in the console. You will also be required to choose the Witness Region, which receives data written to any peered Region but doesn’t have an endpoint. Choose Create cluster. If you already have a remote Region cluster, you can optionally enter its ARN.

Next, add an existing remote cluster or create your second cluster in another Region by choosing Create cluster.

Now, you can create the second cluster with your peer cluster ARN as the first cluster.

When the second cluster is created, you must peer the cluster in us-east-1 in order to complete the multi-Region creation.

Go to the first cluster page and choose Peer to confirm cluster peering for both clusters.

Now, your multi-Region cluster is created successfully. You can see details about the peers that are in other Regions in the Peers tab.

To get hands-on experience with Aurora DSQL, you can use this step-by-step workshop. It walks through the architecture, key considerations, and best practices as you build a sample retail rewards point application with active-active resiliency.

You can use the AWS SDKs, AWS Comand Line Interface (AWS CLI), and Aurora DSQL APIs to create and manage Aurora DSQL programmatically. To learn more, visit Setting up Aurora DSQL clusters in the Amazon Aurora DSQL User Guide.

What did we add after the preview?
We used your feedback and suggestions during the preview period to add new capabilities. We’ve highlighted a few of the new features and capabilities:

  • Console experience –We improved your cluster management experience to create and peer multi-Region clusters as well as easily connect using AWS CloudShell.
  • PostgreSQL features – We added support for views, unique secondary indexes for tables with existing data and launched Auto-Analyze which removes the need to manually maintain accurate table statistics. Learn about Aurora DSQL PostgreSQL-compatible features.
  • Integration with AWS services –We integrated various AWS services such as AWS Backup for a full snapshot backup and Aurora DSQL cluster restore, AWS PrivateLink for private network connectivity, AWS CloudFormation for managing Aurora DSQL resources, and AWS CloudTrail for logging Aurora DSQL operations.

Aurora DSQL now provides a Model Context Protocol (MCP) server to improve developer productivity by making it easy for your generative AI models and database to interact through natural language. For example, install Amazon Q Developer CLI and configure Aurora DSQL MCP server. Amazon Q Developer CLI now has access to an Aurora DSQL cluster. You can easily explore the schema of your database, understand the structure of the tables, and even execute complex SQL queries, all without having to write any additional integration code.

Now available
Amazon Aurora DSQL is available today in the AWS US East (N. Virginia), US East (Ohio), US West (Oregon) Regions for single- and multi-Region clusters (two peers and one witness Region), Asia Pacific (Osaka) and Asia Pacific (Tokyo) for single-Region clusters, and Europe (Ireland), Europe (London), and Europe (Paris) for single-Region clusters.

You’re billed on a monthly basis using a single normalized billing unit called Distributed Processing Unit (DPU) for all request-based activity such as read/write. Storage is based on the total size of your database and measured in GB-months. You are only charged for one logical copy of your data per single-Region cluster or multi-Region peered cluster. As a part of the AWS Free Tier, your first 100,000 DPUs and 1 GB-month of storage each month is free. To learn more, visit Amazon Aurora DSQL Pricing.

Give Aurora DSQL a try for free in the Aurora DSQL console. For more information, visit the Aurora DSQL User Guide and send feedback to AWS re:Post for Aurora DSQL or through your usual AWS support contacts.

— Channy

Join AWS Cloud Infrastructure Day to learn cutting-edge innovations building global cloud infrastructure

Post Syndicated from Channy Yun (윤석찬) original https://aws.amazon.com/blogs/aws/join-aws-cloud-infrastructure-day-to-learn-cutting-edge-innovations-building-global-cloud-infrastructure/

I want to introduce the AWS Cloud Infrastructure Day to provide a comprehensive showcase of latest innovations in AWS cloud infrastructure. This event will highlight cutting-edge advances across compute, artificial intelligence and machine learning (AI/ML), storage solutions, networking capabilities, serverless, and accelerated technologies, and global infrastructure.

Join us for AWS Cloud Infrastructure Day, a free-to-attend one-day virtual event on May 22, 2025, starting at 11:00 AM PDT (2:00 PM EDT). We will stream the event simultaneously across multiple platforms, including LinkedIn Live, Twitter, YouTube, and Twitch.

Here are some of the highlights you can expect from this event:

Willem Visser, VP of EC2 Technology will open with the introduction of the AWS journey since 2006, when Amazon Elastic Compute Cloud (Amazon EC2) was launched with the goal of customer-obsessed innovation. He will speak about the progress made over nearly two decades in cloud infrastructure to support both startups and enterprise workloads based on scale, capacity, and flexibility.

You can learn how AWS developed beyond computing instances to create a complete cloud infrastructure, including the parallel evolution of services like storage and networking capabilities.

Todd Kennedy, Principal Engineer, GoDaddy, will share GoDaddy’s Graviton adoption journey and the benefits it reaped from Graviton. Todd will walk through an example to demonstrate moving Rust workloads to Graviton. Learn how GoDaddy achieved 40 percent compute cost savings and over 20 percent performance gains.

This event covers a variety of topics related to AWS Cloud infrastructure. Here are interesting topics that caught my interest:

  • Generative AI at the edge – You can learn how to select, fine-tune, and deploy small language models (SLMs) for on-premises and edge use cases due to data residency requirements using AWS hybrid and edge services.
  • Serverless for agentic AI auditability – You can learn how AWS Step Functions and AWS Lambda transform opaque agentic AI system operations into transparent, auditable workflows.
  • Accelerated computing – You can get a close look at AWS innovation across silicon, server, and data centers and learn how customers are using AI chips. Learn how you can get started and reduce your generative AI costs.
  • Networking capability – You can learn how AWS infrastructure—from physical fiber to software-defined networking—enables unparalleled performance and reliability at global scale. The session covers modern application networking patterns while emphasizing secure connectivity solutions for hybrid environments.

This event is perfect for technical decision-makers and developers and offers deep technical insights and hands-on demonstrations of the latest AWS Cloud infrastructure solutions.

To learn more details, review the event schedule and register for AWS Cloud Infrastructure Day.

— Channy

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