Dev Signal Guide
Senior developers running RAG, semantic search, or recommendation workloads on DynamoDB who want to eliminate a separate vector database from their stack.
DynamoDB now supports native vector search through a new SearchVectors API, letting you store embeddings and query vectors directly inside existing DynamoDB tables. This eliminates the need to run a separate vector database alongside your application data, removing the sync overhead and pipeline latency that comes with dual-system architectures. The feature is production-ready and targets RAG pipelines, semantic search, and recommendation workloads already built on DynamoDB.
Dev Signal Verdict
Best for: Senior developers running RAG, semantic search, or recommendation workloads on DynamoDB who want to eliminate a separate vector database from their stack.
Start with a proof-of-concept if you are currently copying data between DynamoDB and a vector store; the consolidated model reduces operational burden, but model your vector operation costs before committing to full migration.
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No. The native SearchVectors API lets you store embeddings and query vectors directly in DynamoDB tables, removing the need for systems like Pinecone, Weaviate, or Milvus.
You can use embedding models from AWS Bedrock, Cohere, or OpenAI. You must choose and configure your model when setting up the vector index.
Vector operations are billed separately from standard DynamoDB pricing, charged per GB across writes, reads, and storage. Model costs carefully before production rollout.
You must configure a vector index specifying the number of dimensions and the distance function, then rewrite relevant queries to use the SearchVectors API.
Yes, the feature is marked production-ready. AWS recommends starting with a proof-of-concept if you are currently syncing data between DynamoDB and a standalone vector store.
Based on Dev Signal coverage