Amazon S3 Vectors now generally available with increased scale and performance | AWS News Blog (opens in new tab)

Amazon S3 Vectors has reached general availability, establishing the first cloud object storage service with native support for storing and querying vector data. This serverless solution allows organizations to reduce total ownership costs by up to 90% compared to specialized vector database solutions while providing the performance required for production-grade AI applications. By integrating vector capabilities directly into S3, AWS enables a simplified architecture for retrieval-augmented generation (RAG), semantic search, and multi-agent workflows.

Massive Scale and Index Consolidation

The move to general availability introduces a significant increase in data capacity, allowing users to manage massive datasets without complex infrastructure workarounds.

• Increased Index Limits: Each index can now store and search across up to 2 billion vectors, representing a 40x increase from the 50 million limit during the preview phase.
• Bucket Capacity: A single vector bucket can now scale to house up to 20 trillion vectors.
• Simplified Architecture: The increased scale per index removes the need for developers to shard data across multiple indexes or implement custom query federation logic.

Performance and Latency Optimizations

The service has been tuned to meet the low-latency requirements of interactive applications like conversational AI and real-time inference.

• Query Response Times: Frequent queries now achieve latencies of approximately 100ms or less, while infrequent queries consistently return results in under one second.
• Enhanced Retrieval: Users can now retrieve up to 100 search results per query (increased from 30), providing broader context for RAG applications.
• Write Throughput: The system supports up to 1,000 PUT transactions per second for streaming single-vector updates, ensuring new data is immediately searchable.

Serverless Efficiency and Ecosystem Integration

S3 Vectors functions as a fully serverless offering, eliminating the need to provision or manage underlying instances while paying only for active storage and queries.

• Amazon Bedrock Integration: It is now generally available as a vector storage engine for Bedrock Knowledge Bases, facilitating the building of RAG applications.
• OpenSearch Support: Integration with Amazon OpenSearch allows users to utilize S3 Vectors for storage while leveraging OpenSearch for advanced analytics and search features.
• Expanded Footprint: The service is now available in 14 AWS Regions, up from five during the preview period.

With its massive scale and 90% cost reduction, S3 Vectors is a primary candidate for organizations looking to move AI prototypes into production. Developers should consider migrating high-volume vector workloads to S3 Vectors to benefit from the serverless operational model and the native integration with the broader AWS AI stack.