AWS is finally adding native vector search to its managed NoSQL database DynamoDB, which is typically used to store high-volume operational and transactional data.

The update, according to analysts, removes complexity for development teams that are trying to maintain separate vector databases for a rapidly growing class of AI and agentic applications that rely on real-time access to operational and transactional data to improve the accuracy and relevance of their responses.

“This collapses a common two-database architecture into one operational data layer. Developers can update an item and its vector representation together, use familiar DynamoDB APIs, and avoid building a separate synchronization pipeline. That should materially shorten time-to-market for AI features built around existing DynamoDB data,” said Stephanie Walter, practice lead of the AI stack at HyperFRAME Research.

Prior to the update, enterprises using DynamoDB typically had to copy data into OpenSearch or another vector database, such as Pinecone and Weaviate, often using DynamoDB Streams or custom pipelines, which meant operating two data layers and managing embedding generation, backfills, retries, schema changes, security policies, and synchronization, Walter noted.