DynamoDB Adds Real-Time Vector Search Without a Separate Database
General availability of vector search in DynamoDB lets teams store embeddings alongside operational data and run similarity search without replicating data into a separate vector database.
DynamoDB Adds Real-Time Vector Search Without a Separate Database
AWS announced the general availability of vector search in Amazon DynamoDB on August 5, 2026. The change matters for systems combining operational data with AI features because developers can store embeddings alongside application records and run similarity searches directly against that data.
Why can this simplify architecture?
Many designs keep operational data in one database and copy embeddings into a separate vector store. That creates synchronization logic, additional pipelines, and the possibility of inconsistent copies. Native vector search in DynamoDB can remove part of that architecture when the workload fits the service.
Useful scenarios include semantic search, similar-item retrieval, recommendations, and some RAG architectures. Database selection should still depend on workload patterns, cost, indexing needs, and query requirements. The important change is that AWS teams now have a simpler option for combining real-time operational data and vector similarity search inside one managed system.