Amazon DynamoDB adds real-time vector search at massive scale

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AWS has announced the general availability of native vector search for Amazon DynamoDB, its managed NoSQL database. The feature delivers single-digit millisecond latency at 99%+ recall even at trillion-vector scale, addressing the traditional trade-off between speed, scale, and accuracy in vector search. Vectors (numerical representations of data used to find semantically similar items) can be stored alongside regular attributes and generated using any model, including those on Amazon Bedrock.

For agent builders specifically, AWS calls out that DynamoDB already supports storing memory for AI agents, and vector search now adds semantic retrieval over that memory — enabling 'agentic grounding,' where agents retrieve relevant context to improve accuracy. Additional use cases include retrieval-augmented generation (RAG, a technique where a model queries an external knowledge store before answering), product similarity search, and recommendation systems. The feature is serverless, requires no infrastructure management, and is available in all commercial AWS and GovCloud regions.