Post Syndicated from Sohaib Katariwala original https://aws.amazon.com/blogs/big-data/optimizing-vector-search-using-amazon-s3-vectors-and-amazon-opensearch-service/
NOTE: As of July 15, the Amazon S3 Vectors Integration with Amazon OpenSearch Service is in preview release and is subject to change.
The way we store and search through data is evolving rapidly with the advancement of vector embeddings and similarity search capabilities. Vector search has become essential for modern applications such as generative AI and agentic AI, but managing vector data at scale presents significant challenges. Organizations often struggle with the trade-offs between latency, cost, and accuracy when storing and searching through millions or billions of vector embeddings. Traditional solutions either require substantial infrastructure management or come with prohibitive costs as data volumes grow.
We now have a public preview of two integrations between Amazon Simple Storage Service (Amazon S3) Vectors and Amazon OpenSearch Service that give you more flexibility in how you store and search vector embeddings:
- Cost-optimized vector storage: OpenSearch Service managed clusters using service-managed S3 Vectors for cost-optimized vector storage. This integration will support OpenSearch workloads that are willing to trade off higher latency for ultra-low cost and still want to use advanced OpenSearch capabilities (such as hybrid search, advanced filtering, geo filtering, and so on).
- One-click export from S3 Vectors: One-click export from an S3 vector index to OpenSearch Serverless collections for high-performance vector search. Customers who build natively on S3 Vectors will benefit from being able to use OpenSearch for faster query performance.
By using these integrations, you can optimize cost, latency, and accuracy by intelligently distributing your vector workloads by keeping infrequent queried vectors in S3 Vectors and using OpenSearch for your most time-sensitive operations that require advanced search capabilities such as hybrid search and aggregations. Further, OpenSearch performance tuning capabilities (that is, quantization, k-nearest neighbor (knn) algorithms, and method-specific parameters) help to improve the performance with little compromise of cost or accuracy.
In this post, we walk through this seamless integration, providing you with flexible options for vector search implementation. You’ll learn how to use the new S3 Vectors engine type in OpenSearch Service managed clusters for cost-optimized vector storage and how to use one-click export from S3 Vectors to OpenSearch Serverless collections for high-performance scenarios requiring sustained queries with latency as low as 10ms. By the end of this post, you’ll understand how to choose and implement the right integration pattern based on your specific requirements for performance, cost, and scale.
Service overview
Amazon S3 Vectors is the first cloud object store with native support to store and query vectors with sub-second search capabilities, requiring no infrastructure management. It combines the simplicity, durability, availability, and cost-effectiveness of Amazon S3 with native vector search functionality, so you can store and query vector embeddings directly in S3. Amazon OpenSearch Service provides two complementary deployment options for vector workloads: Managed Clusters and Serverless Collections. Both harness Amazon OpenSearch’s powerful vector search and retrieval capabilities, though each excels in different scenarios. For OpenSearch users, the integration between S3 Vectors and Amazon OpenSearch Service offers unprecedented flexibility in optimizing your vector search architecture. Whether you need ultra-fast query performance for real-time applications or cost-effective storage for large-scale vector datasets, this integration lets you choose the approach that best fits your specific use case.
Understanding Vector Storage Options
OpenSearch Service provides multiple options for storing and searching vector embeddings, each optimized for different use cases. The Lucene engine, which is OpenSearch’s native search library, implements the Hierarchical Navigable Small World (HNSW) method, offering efficient filtering capabilities and strong integration with OpenSearch’s core functionality. For workloads requiring additional optimization options, the Faiss engine (Facebook AI Similarity Search) provides implementations of both HNSW and IVF (Inverted File Index) methods, along with vector compression capabilities. HNSW creates a hierarchical graph structure of connections between vectors, enabling efficient navigation during search, while IVF organizes vectors into clusters and searches only relevant subsets during query time. With the introduction of the S3 engine type, you now have a cost-effective option that uses Amazon S3’s durability and scalability while maintaining sub-second query performance. With this variety of options, you can choose the most suitable approach based on your specific requirements for performance, cost, and accuracy. For instance, if your application requires sub-50 ms query responses with efficient filtering, Faiss’s HNSW implementation is the best choice. Alternatively, if you need to optimize storage costs while maintaining reasonable performance, the new S3 engine type would be more appropriate.
Solution overview
In this post, we explore two primary integration patterns:
OpenSearch Service managed clusters using service-managed S3 Vectors for cost-optimized vector storage.
For customers already using OpenSearch Service domains who want to optimize costs while maintaining sub-second query performance, the new Amazon S3 engine type offers a compelling solution. OpenSearch Service automatically manages vector storage in Amazon S3, data retrieval, and cache optimization, eliminating operational overhead.
One-click export from an S3 vector index to OpenSearch Serverless collections for high-performance vector search.
For use cases requiring faster query performance, you can migrate your vector data from an S3 vector index to an OpenSearch Serverless collection. This approach is ideal for applications that require real-time response times and gives you the benefits that come with Amazon OpenSearch Serverless, including advanced query capabilities and filters, automatic scaling and high availability, and no administration. The export process automatically handles schema mapping, vector data transfer, index optimization, and connection configuration.
The following illustration shows the two integration patterns between Amazon OpenSearch Service and S3 Vectors.

Prerequisites
Before you begin, make sure you have:
- An AWS account
- Access to Amazon S3 and Amazon OpenSearch Service
- An OpenSearch Service domain (for the first integration pattern)
- Vector data stored in S3 Vectors (for the second integration pattern)
Integration pattern 1: OpenSearch Service managed cluster using S3 Vectors
To implement this pattern:
- Create an OpenSearch Service Domain using OR1 instances on OpenSearch version 2.19.
- While creating the OpenSearch Service domain, choose the Enable S3 Vectors as an engine option in the Advanced features section.
- Sign in to OpenSearch Dashboards and open Dev tools. Then create your knn index and specify s3vector as the engine.
- Index your vectors using the Bulk API:
- Run a knn query as usual:
The following animation demonstrates steps 2-4 above.

Integration pattern 2: Export S3 vector indexes to OpenSearch Serverless
To implement this pattern:
- Navigate to the AWS Management Console for Amazon S3 and select your S3 vector bucket.

- Select a vector index that you want to export. Under Advanced search export, select Export to OpenSearch.

Alternatively, you can:
- Navigate to the OpenSearch Service console.
- Select Integrations from the navigation pane.
- Here you will see a new Integration Template to Import S3 vectors to OpenSearch vector engine – preview. Select Import S3 vector index.

- You will now be brought to the Amazon OpenSearch Service integration console with the Export S3 vector index to OpenSearch vector engine template pre-selected and pre-populated with your S3 vector index Amazon Resource Name (ARN). Select an existing role that has the necessary permissions or create a new service role.

- Scroll down and choose Export to start the steps to create a new OpenSearch Serverless collection and copy data from your S3 vector index into an OpenSearch knn index.

- You will now be taken to the Import history page in the OpenSearch Service console. Here you will see the new job that was created to migrate your S3 vector index into the OpenSearch serverless knn index. After the status changes from In Progress to Complete, you can connect to the new OpenSearch serverless collection and query your new OpenSearch knn index.

The following animation demonstrates how to connect to the new OpenSearch serverless collection and query your new OpenSearch knn index using Dev tools.

Cleanup
To avoid ongoing charges:
- For Pattern 1:
- Delete the OpenSearch index using S3 vectors.
- Delete the OpenSearch Service managed cluster if no longer needed.
- For Pattern 2:
- Delete the import task from the Import history section of the OpenSearch Service console. Deleting this task will remove both the OpenSearch vector collection and the OpenSearch Ingestion pipeline that was automatically created by the import task.
Conclusion
The innovative integration between Amazon S3 Vectors and Amazon OpenSearch Service marks a transformative milestone in vector search technology, offering unprecedented flexibility and cost-effectiveness for enterprises. This powerful combination delivers the best of both worlds: The renowned durability and cost efficiency of Amazon S3 merged seamlessly with the advanced AI search capabilities of OpenSearch. Organizations can now confidently scale their vector search solutions to billions of vectors while maintaining control over their latency, cost, and accuracy. Whether your priority is ultra-fast query performance with latency as low as 10ms through OpenSearch Service, or cost-optimized storage with impressive sub-second performance using S3 Vectors or implementing advanced search capabilities in OpenSearch, this integration provides the perfect solution for your specific needs. We encourage you to get started today by trying S3 Vectors engine in your OpenSearch managed clusters and testing the one-click export from S3 vector indexes to OpenSearch Serverless.
For more information, visit:
- Amazon S3 Vectors documentation
- Amazon OpenSearch Service documentation
- OpenSearch Service integration with Amazon S3 Vectors
- Amazon OpenSearch Service Vector database blog
About the Authors
Sohaib Katariwala is a Senior Specialist Solutions Architect at AWS focused on Amazon OpenSearch Service based out of Chicago, IL. His interests are in all things data and analytics. More specifically he loves to help customers use AI in their data strategy to solve modern day challenges.
Mark Twomey is a Senior Solutions Architect at AWS focused on storage and data management. He enjoys working with customers to put their data in the right place, at the right time, for the right cost. Living in Ireland, Mark enjoys walking in the countryside, watching movies, and reading books.
Sorabh Hamirwasia is a senior software engineer at AWS working on the OpenSearch Project. His primary interest include building cost optimized and performant distributed systems.
Pallavi Priyadarshini is a Senior Engineering Manager at Amazon OpenSearch Service leading the development of high-performing and scalable technologies for search, security, releases, and dashboards.
Bobby Mohammed is a Principal Product Manager at AWS leading the Search, GenAI, and Agentic AI product initiatives. Previously, he worked on products across the full lifecycle of machine learning, including data, analytics, and ML features on SageMaker platform, deep learning training and inference products at Intel.
Angel Conde Manjon is a Sr. EMEA Data & AI PSA, based in Madrid. He has previously worked on research related to Data Analytics and Artificial Intelligence in diverse European research projects. In his current role, Angel helps partners develop businesses centered on Data and AI.
Diego Colombatto is a Principal Partner Solutions Architect at AWS. He brings more than 15 years of experience in designing and delivering Digital Transformation projects for enterprises. At AWS, Diego works with partners and customers advising how to leverage AWS technologies to translate business needs into solutions. Solution architectures, algorithmic trading and cooking are some of his passions and he’s always open to start a conversation on these topics.
Sandeep Adwankar is a Senior Technical Product Manager at AWS. Based in the California Bay Area, he works with customers around the globe to translate business and technical requirements into products that enable customers to improve how they manage, secure, and access data.































Amit Maindola is a Senior Data Architect focused on data engineering, analytics, and AI/ML at Amazon Web Services. He helps customers in their digital transformation journey and enables them to build highly scalable, robust, and secure cloud-based analytical solutions on AWS to gain timely insights and make critical business decisions.
Srinivas Kandi is a Senior Architect at Stifel focusing on delivering the next generation of cloud data platform on AWS. Prior to joining Stifel, Srini was a delivery specialist in cloud data analytics at AWS helping several customers in their transformational journey into AWS cloud. In his free time, Srini likes to explore cooking, travel and learn new trends and innovations in AI and cloud computing.
Hossein Johari is a seasoned data and analytics leader with over 25 years of experience architecting enterprise-scale platforms. As Lead and Senior Architect at Stifel Financial Corp. in St. Louis, Missouri, he spearheads initiatives in Data Platforms and Strategic Solutions, driving the design and implementation of innovative frameworks that support enterprise-wide analytics, strategic decision-making, and digital transformation. Known for aligning technical vision with business objectives, he works closely with cross-functional teams to deliver scalable, forward-looking solutions that advance organizational agility and performance.
Ahmad Rawashdeh is a Senior Architect at Stifel Financial. He supports Stifel and its clients in designing, implementing, and building scalable and reliable data architectures on Amazon Web Services (AWS), with a strong focus on data lake strategies, database services, and efficient data ingestion and transformation pipelines.
Lei Meng is a data architect at Stifel. His focus is working in designing and implementing scalable and secure data solutions on the AWS and helping Stifel’s cloud migration from on-premises systems.

































Pratik Patel is Sr. Technical Account Manager and streaming analytics specialist. He works with AWS customers and provides ongoing support and technical guidance to help plan and build solutions using best practices and proactively keep customers’ AWS environments operationally healthy.
Amar is a seasoned Data Analytics specialist at AWS UK, who helps AWS customers to deliver large-scale data solutions. With deep expertise in AWS analytics and machine learning services, he enables organizations to drive data-driven transformation and innovation. He is passionate about building high-impact solutions and actively engages with the tech community to share knowledge and best practices in data analytics.
Priyanka Chaudhary is a Senior Solutions Architect and data analytics specialist. She works with AWS customers as their trusted advisor, providing technical guidance and support in building Well-Architected, innovative industry solutions.

















If you select Redshift, you will be transferred to the Query editor where you can execute the SQL and see the results as shown in the following figure.
Avijit Goswami is a Principal Data Solutions Architect at AWS specialized in data and analytics. He supports AWS strategic customers in building high-performing, secure, and scalable data lake solutions on AWS using AWS managed services and open-source solutions. Outside of his work, Avijit likes to travel, hike in the San Francisco Bay Area trails, watch sports, and listen to music.
Saman Irfan is a Senior Specialist Solutions Architect focusing on Data Analytics at Amazon Web Services. She focuses on helping customers across various industries build scalable and high-performant analytics solutions. Outside of work, she enjoys spending time with her family, watching TV series, and learning new technologies.
Sudarshan Narasimhan is a Principal Solutions Architect at AWS specialized in data, analytics and databases. With over 19 years of experience in Data roles, he is currently helping AWS Partners & customers build modern data architectures. As a specialist & trusted advisor he helps partners build & GTM with scalable, secure and high performing data solutions on AWS. In his spare time, he enjoys spending time with his family, travelling, avidly consuming podcasts and being heartbroken about Man United’s current state.












Laura is an Identity Solutions Architect at AWS, where she thrives on helping customers overcome security and identity challenges. In her free time, she enjoys wreck diving and traveling around the world.
Yanzhu Ji is a Product Manager in the Amazon Redshift team. She has experience in product vision and strategy in industry-leading data products and platforms. She has outstanding skill in building substantial software products using web development, system design, database, and distributed programming techniques. In her personal life, Yanzhu likes painting, photography, and playing tennis.


















Charlie can now further update the SQL query and use it to power QuickSight dashboards that can be shared with Sales team members.
Sandeep Adwankar is a Senior Technical Product Manager at AWS. Based in the California Bay Area, he works with customers around the globe to translate business and technical requirements into products that enable customers to improve how they manage, secure, and access data.
Srividya Parthasarathy is a Senior Big Data Architect on the AWS Lake Formation team. She works with the product team and customers to build robust features and solutions for their analytical data platform. She enjoys building data mesh solutions and sharing them with the community.























