Skip to main content
Hello and welcome back. In this lesson we’ll explore S3 Vector Buckets — Amazon S3’s native support for storing and querying vector embeddings directly inside S3. This capability lets you keep embeddings and perform similarity search without deploying a separate vector database, simplifying architecture and lowering operational cost. Why this matters
  • Vector embeddings power semantic search, recommendation, and retrieval-augmented generation (RAG). Storing and querying embeddings natively in S3 reduces data movement, simplifies security and lifecycle management, and leverages S3’s serverless scale.
Analogy
  • A regular S3 bucket is like a giant filing cabinet where you store files and retrieve them by filename or key.
  • S3 Vector Buckets extend that cabinet so it can “understand” the content and let you search by similarity rather than just name or path.
Learn more about Amazon S3: Amazon S3 overview
S3 Vector Buckets provide serverless, scalable vector storage and similarity search directly within S3, reducing operational overhead and often lowering costs compared to dedicated vector databases.
Overview: what S3 Vector Buckets provide
  • Native vector storage and similarity search inside S3.
  • Serverless operation: no infrastructure to provision or manage.
  • Integration with AWS ecosystem (e.g., Bedrock Knowledge Bases) and export paths to analytics/search services like OpenSearch.
The image is a graphic for Amazon S3 Vector Buckets, highlighting features like cost reduction, serverless architecture, and massive scaling capabilities, with details about preview and availability dates.
Key benefits (at-a-glance) Preview, GA and availability timeline S3 Vector Buckets progressed quickly from preview to general availability and expanded both features and regional reach.
  • Preview (July 14, 2025): initial capabilities, integration with Bedrock Knowledge Bases, and export support to OpenSearch.
  • GA (December 2, 2025): increased limits and performance, expanded to 14 AWS Regions, added CloudFormation support, PrivateLink, tagging, and higher write throughput.
The image depicts a timeline for the introduction of S3 Vector Buckets, highlighting key milestones: the preview launch in July 2025, general availability by December 2025, and early traction metrics by November 2025.
Preview vs GA — highlights and limits Early traction and ecosystem adoption
  • During preview, customers and partners tested large workloads: hundreds of thousands of vector indexes and tens of billions of vectors.
  • By November 2025 some adopters executed over a billion similarity queries against S3 Vector Buckets.
  • Ecosystem integrations (for example, Supabase) began appearing quickly, signaling strong interest from the developer community.
Final thoughts S3 Vector Buckets bring scalable, cost-effective vector search into the S3 ecosystem. They are an attractive option when you want to:
  • Simplify architecture by co-locating objects and embeddings in the same store.
  • Reduce operational overhead with serverless scaling.
  • Leverage AWS integrations for production ML and search workflows.
Next steps / hands-on demo In the next lesson we’ll walk through a hands-on demo: creating an S3 Vector Bucket, ingesting embeddings, and running similarity queries so you can see the end-to-end workflow. References and further reading See you in the next lesson.

Watch Video