Amazon DynamoDB now supports real-time vector search at any scale

Vector search has become essential for modern AI applications, but implementing it at scale has traditionally required separate infrastructure. If you’ve built RAG (Retrieval-Augmented Generation) systems or semantic search features, you know the complexity: managing a dedicated vector database like Pinecone or Milvus, syncing data between systems, and handling scalability yourself. AWS is simplifying this with native vector search built directly into DynamoDB. The service now supports single-digit millisecond query latency with 99%+ recall—meaning you can search trillions of vectors stored in DynamoDB without deploying additional infrastructure.

Here’s how it works technically. DynamoDB stores your vector embeddings alongside your regular data in the same items, eliminating the need for separate databases and ETL pipelines. When you query, DynamoDB uses highly optimized indexing algorithms to search through these vectors efficiently. You simply enable vector indexing on specific attributes, then use the new vector search API to find similar items by distance metrics like Euclidean or cosine similarity. Since DynamoDB is serverless and managed, you don’t provision capacity for the vector index—it scales automatically. The 99%+ recall figure is particularly important: it means the results are nearly as accurate as exhaustive search, but orders of magnitude faster.

The practical value becomes clear in real scenarios. E-commerce teams can build product recommendations by storing product embeddings in DynamoDB alongside inventory and pricing data, then searching for similar items in milliseconds when a customer views a product. Customer support teams can use vector search to find relevant past tickets and solutions instantly. Content platforms can power semantic search without maintaining separate infrastructure. Healthcare applications can match patient records or medical images to similar cases. Because everything lives in one database, your application code becomes simpler—you’re querying one source of truth rather than orchestrating reads across multiple systems.

The serverless nature deserves emphasis. You don’t manage vector index capacity, worry about sharding strategies, or handle failover. DynamoDB handles 99.99% availability and multi-region replication if you need it. You pay only for the vectors you store and queries you run, similar to standard DynamoDB pricing. This is particularly valuable for teams without dedicated database infrastructure expertise or those building applications where vector search is a feature, not the core product. If you’re currently maintaining a separate vector database alongside DynamoDB, consolidating to native vector search reduces operational overhead significantly.