AWS has announced native vector search for Amazon DynamoDB, allowing developers to store vector embeddings alongside operational data and perform approximate nearest neighbor searches without replicating data to a dedicated vector database.
The serverless capability delivers single-digit millisecond latency with more than 99% recall and is designed to scale to trillions of vectors.
Developers can create vector indexes, apply attribute-based filtering, and use Euclidean, cosine, or dot product distance metrics to power semantic search, retrieval augmented generation (RAG), AI agent memory, recommendation engines, and personalization workloads while continuing to use familiar DynamoDB APIs and infrastructure.
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