Amazon S3 Vectors
A native vector storage capability introduced by AWS in 2025 that provides vector indexing and approximate nearest neighbor query functionality directly on top of Amazon S3 object storage. It is billed based on storage volume and query count, eliminating the need to operate a dedicated vector database.
Timeline (last 90 days)
Amazon S3 Vectors is a native vector storage feature launched by AWS in 2025 that provides vector indexing and approximate nearest-neighbor query capabilities directly on top of S3, without requiring the additional operation of a dedicated vector database.
Amazon S3 Vectors is billed by storage volume and query count, using the pricing and operational model of object storage.
Compared to dedicated vector databases such as Pinecone, Weaviate, and Milvus, S3 Vectors has a significant cost advantage in scenarios with high write-to-read ratios or unpredictable data growth.
Compared to engines optimized specifically for vector retrieval, S3 Vectors may be slightly inferior in query latency and high-concurrency throughput, and this trade-off needs to be weighed against SLA requirements when making a selection.
Amazon S3 Vectors can be implemented as a custom memory provider for NAT by following NAT's memory interface specifications to connect vector storage, query, and update operations to the S3 Vectors API.
All Facts (5)
Amazon S3 Vectors is a native vector storage feature launched by AWS in 2025 that provides vector indexing and approximate nearest-neighbor query capabilities directly on top of S3, without requiring the additional operation of a dedicated vector database.
50%UnverifiedAmazon S3 Vectors is billed by storage volume and query count, using the pricing and operational model of object storage.
50%UnverifiedCompared to dedicated vector databases such as Pinecone, Weaviate, and Milvus, S3 Vectors has a significant cost advantage in scenarios with high write-to-read ratios or unpredictable data growth.
50%UnverifiedCompared to engines optimized specifically for vector retrieval, S3 Vectors may be slightly inferior in query latency and high-concurrency throughput, and this trade-off needs to be weighed against SLA requirements when making a selection.
50%UnverifiedAmazon S3 Vectors can be implemented as a custom memory provider for NAT by following NAT's memory interface specifications to connect vector storage, query, and update operations to the S3 Vectors API.
50%