vector-search dynamodb rag serverless embeddings

DynamoDB adds native vector search without separate database

Store embeddings and query vectors directly in DynamoDB tables via new SearchVectors API; eliminates dual-system sync overhead.

Summary

Removes architectural complexity of maintaining separate vector databases—embeddings and application data live in one managed system. Reduces data pipeline latency and operational burden for RAG, semantic search, and recommendation workloads.

Why it matters

Implementation verdict

Replaces Pinecone/Weaviate/Milvus for teams already on DynamoDB; requires choosing embedding model (Bedrock, Cohere, OpenAI), configuring vector index (dimensions, distance function), and rewriting queries to SearchVectors API. Production-ready now, but cost-sensitive: vector operations billed separately per GB (writes, reads, storage). Start with proof-of-concept if you're currently copying data between systems.

Sources

1.up to 4096 dimensions, Euclidean, Cosine, and Dot product distance functions, and inline filtering
2.Vector indexes have no storage limits and scale horizontally as your data grows
3.A vector index is billed on three dimensions, in addition to the standard DynamoDB charges for the underlying table
4.This scales as big as you want it (think trillions of vectors) while maintaining single-digit millisecond latency

Dev Signal

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