DynamoDB Adds Built-In Vector Search, Eliminating Need for Separate Databases

Amazon has rolled out native vector search capabilities for DynamoDB, letting developers embed similarity searches directly into their NoSQL database without maintaining a separate vector database. The feature stores embeddings alongside application data in the same table, cutting architectural complexity and eliminating duplicate data pipelines.

How It Works#

The new functionality relies on a dedicated DynamoDB index type that stores and retrieves vector embeddings using approximate nearest-neighbor queries. Developers select an embedding model—such as Amazon Bedrock Titan Text Embeddings, Cohere Embed, or OpenAI's offerings—then define a vector index by specifying dimensions and a distance metric. The new SearchVectors API powers the actual queries.

The system supports up to 4,096 dimensions and offers three distance functions: Euclidean, Cosine, and Dot product. Queries can include inline filtering to narrow results based on attributes alongside vector similarity.

Key Advantages#

Unified storage: Previously, teams had to copy data into a separate vector database and keep both systems in sync. Combining embeddings and transactional data in one place reduces moving parts and operational overhead.

Serverless scaling: DynamoDB's fully managed nature means vector indexes scale automatically with demand—no infrastructure provisioning needed. According to AWS, the feature can handle trillions of vectors while maintaining single-digit millisecond latency.

Cost considerations: Vector indexes are billed separately from standard DynamoDB charges on three dimensions: bytes written to the index, bytes processed during searches, and bytes stored. AWS recommends techniques like using lower dimensions, limiting index projections, and selective partitioning to reduce costs.

Real-World Use Cases#

The feature opens doors for semantic search on agentic memory, retrieval-augmented generation pipelines, recommendation engines, personalized user experiences, and anomaly detection—anywhere applications need meaning-based rather than keyword-based lookups.

Community Reception#

Industry response has been largely positive, though some note AWS enters a crowded field: over recent years. Developers have raised questions about filter execution order and noted potential cost tradeoffs versus alternatives like S3 vector buckets, which offer cheaper storage but higher latency.