DynamoDB Now Supports Vector Search: No Separate Database Required
Cloud Platform EngineeringThe two-database problem for AI applications — one for operational data, one for vector embeddings — has been a standard architectural pain point. DynamoDB just removed the requirement to maintain both.
What Happened
Amazon DynamoDB now supports native vector search across all commercial AWS Regions and GovCloud, announced generally available by AWS with immediate access for existing DynamoDB users. The implementation stores vector embeddings directly alongside operational data in DynamoDB tables and executes similarity searches through a new vector index type and SearchVectors API.
The specification supports up to 4,096 dimensions, Euclidean, Cosine, and Dot product distance functions, and up to 100 results per query with optional metadata filtering. Embeddings are generated via Amazon Bedrock, Cohere, or OpenAI models and stored as float lists using existing DynamoDB calls. The vector index is created as an attribute on the embedding field — no new table or database type required.
Why It Matters
The operational architecture for most RAG and semantic search systems today requires maintaining a separate vector store — Pinecone, pgvector, OpenSearch, Weaviate — synchronized with a primary operational database. That synchronization pipeline introduces latency, operational complexity, and a failure mode: the embeddings and the source data they index can drift out of sync, producing retrieved results that no longer match current records. DynamoDB’s native approach eliminates the synchronization layer entirely by keeping embeddings in the same table as the data they represent.
The pricing model follows DynamoDB’s standard pay-per-request structure rather than introducing a new pricing dimension for vector operations. For teams already using DynamoDB for operational data, the incremental cost of adding vector search is predictable and calculated on the same model as existing table operations.
For teams building recommendation engines, semantic search, or retrieval-augmented generation pipelines on AWS, the practical simplification is significant: fewer services to provision, monitor, and maintain; a simpler consistency model; and one fewer integration surface to debug under load.
The So What
DynamoDB vector search doesn’t eliminate purpose-built vector databases from every use case — high-dimensional similarity search at extreme scale with advanced filtering may still benefit from a specialized engine. What it eliminates is the need to introduce a separate vector store for the majority of production AI applications where DynamoDB is already the primary data layer. Teams evaluating their stack for new AI feature work should start by evaluating whether DynamoDB native vector search meets their requirements before provisioning a dedicated vector database — the operational simplification is concrete and the performance envelope now covers most production workloads.
Source: AWS News Blog
Content created with AI assistance and reviewed for accuracy.
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