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Amazon DynamoDB now supports real-time vector search at any scale
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Amazon DynamoDB now includes built-in vector search that lets you store embeddings alongside operational data and run similarity queries without a separate vector store.
Engineers can add semantic search to existing DynamoDB applications without the operational overhead of managing a second database or synchronizing data between systems. The feature inherits DynamoDB's serverless scaling and pay-per-request pricing, reducing infrastructure work and cost. It enables use cases such as recommendation engines, retrieval-augmented generation, and anomaly detection directly within the same data store.
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Vector embeddings are stored as a list of numbers in a DynamoDB attribute and indexed with a new vector index type.
Queries are issued through an API that returns ranked results and can filter on non-vector columns.
The service scales automatically, requires no server provisioning, and shares DynamoDB's existing pricing model.
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What elseif makes of it.
The announcement adds a native vector search capability to DynamoDB, removing the need to copy data to a dedicated vector database and maintain a synchronization pipeline. Developers can keep vectors and operational data in the same table, simplifying architecture and reducing data movement costs. This change is most relevant for applications that already rely on DynamoDB for their primary data store.
To use the feature, you generate embeddings with an external model (e.g., from Amazon Bedrock, Cohere, or OpenAI) and store them as a List of Numbers in a table item. You then create a vector index on that attribute, choosing the dimensionality and a distance measure. Searches are performed via an API that takes a query vector, a result limit, and optional filters on other columns, returning the most similar items ranked by distance.
Operationally, the addition eliminates the administrative burden of provisioning, patching, and managing separate vector search infrastructure. Scaling is handled automatically by DynamoDB's serverless backend, and costs follow the existing pay-per-request model. There are no maintenance windows or version upgrades to worry about, which reduces ongoing operational toil.
The solution depends on external embedding generation; DynamoDB does not create the vectors itself. Index creation supports up to a certain dimensionality and a set of distance functions, and filtering is limited to non-vector attributes. Applications that require custom indexing algorithms or specialized distance metrics beyond those provided may need to look elsewhere. Additionally, latency and recall characteristics will vary with index size and query load, so performance testing remains necessary.
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