Purpose-built storage for vectors
"Cost-optimized AI-ready storage with native support for storing and querying vectors at scale,
reducing total costs by up to 90%
Amazon S3 Vectors is the first cloud object store with native support to store and query vectors, delivering purpose-built, cost-optimized vector storage for AI agents, AI inference, and semantic search of your content stored in Amazon S3. By reducing the cost of uploading, storing, and querying vectors by up to 90%, S3 Vectors makes it cost-effective to create and use large vector datasets to improve the memory and context of AI agents as well as semantic search results of your S3 data.
reducing total costs by up to 90%
Amazon S3 Vectors is the first cloud object store with native support to store and query vectors, delivering purpose-built, cost-optimized vector storage for AI agents, AI inference, and semantic search of your content stored in Amazon S3. By reducing the cost of uploading, storing, and querying vectors by up to 90%, S3 Vectors makes it cost-effective to create and use large vector datasets to improve the memory and context of AI agents as well as semantic search results of your S3 data.
Designed to provide the same elasticity, scale, and durability as Amazon S3, S3 Vectors lets you store up to billions of vectors and search data with sub-second query performance. It's ideal for applications that need to build and maintain vector indexes at scale so you can organize and search through massive amounts of information."
- Vector buckets – A new bucket type that's purpose-built to store and query vectors.
- Vector indexes – Within a vector bucket, you can organize your vector data within vector indexes. You perform similarity queries on your vector data within vector indexes.
- Vectors – You store vectors in your vector index. For similarity search and AI applications, vectors are created as vector embeddings which are numerical representations that preserve semantic relationships between content (such as text, images, or audio) so similar items are positioned closer together.
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