Can DynamoDB’s Native Vector Search Replace Vector Databases?

As retrieval-augmented generation, agent memory, and semantic recommendations move into production, many teams have accepted a split architecture: business records stay in a NoSQL database while embeddings are copied to a specialized vector service. Amazon DynamoDB is now challenging that pattern with native vector search.

What changed

The capability is built around a new vector index type. Developers can store an embedding in a table attribute, define the desired dimension and distance function, and issue approximate nearest-neighbor queries through the SearchVectors API. The supported distance functions are Euclidean distance, cosine distance, and dot product. The maximum vector size is 4,096 dimensions, and searches can include inline attribute filters.

DynamoDB does not require a particular embedding provider. Applications can use Amazon Bedrock Titan Text Embeddings, Cohere Embed, or OpenAI text embedding models, then store the resulting vectors alongside ordinary application fields. Potential workloads include semantic search, RAG, recommendation engines, personalization, anomaly detection, and retrieval over an AI agent’s long-term memory.

Why the integration matters

Previously, a DynamoDB-based application generally had to copy records into a separate vector database to support similarity search. That pipeline created more than an extra component. Teams also had to manage dual writes, synchronization delays, deletion propagation, and recovery behavior. Keeping records and embeddings in one DynamoDB table can reduce those consistency and operational concerns.

The model is particularly attractive for organizations already committed to DynamoDB and whose vector workload follows the same data lifecycle as the application. DynamoDB remains serverless, and the vector index is designed to scale horizontally as data grows. No separate vector cluster has to be provisioned or maintained. The feature is available in the regions where DynamoDB is currently offered and supports both Standard and Standard-IA table classes.

The trade-offs remain important

Native support does not mean that vector search is free. In addition to the normal charges for the underlying table, the vector index is billed across three dimensions: data written to the index, data processed during searches, and stored data. All three are measured by bytes and charged per GB. Lower-dimensional embeddings, smaller index projections, omitting vectors from returned results, and selective partitioning can help control the bill.

That makes DynamoDB an expansion of the design space rather than a universal replacement for vector databases. Systems requiring specialized vector operators, complex hybrid retrieval, fine-grained index tuning, or aggressive cost optimization may still prefer a dedicated service. Conversely, teams that value a simpler architecture and already operate DynamoDB should include native search in their evaluation. AWS also plans an ExtendDB adapter for local development and self-managed deployments, so the surrounding ecosystem will be worth watching.

Source: InfoQ 中文