AWS Introduces Native Vector Search for DynamoDB
InfoQ, Monday, August 17th, 2026
Amazon DynamoDB recently introduced native vector search, allowing developers to store embeddings alongside application data and run approximate nearest-neighbor queries directly from DynamoDB without using a separate vector database.
The feature supports filtered similarity searches and configurable vector indexes for semantic search workloads.
Vector search uses a new DynamoDB index type built on vector embeddings stored in table attributes. Developers can choose any embedding model, such as Amazon Bedrock Titan Text Embeddings, Cohere Embed, or OpenAI text embedding models, create a vector index with the required dimensions and distance function, and query it using the new SearchVectors API. Esra Kayabali, principal solutions architect at AWS, writes:
"Vector indexes have no storage limits and scale horizontally as your data grows. You can now build applications that require semantic retrieval on agentic memory, retrieval augmented generation, recommendation engines, personalized experiences, anomaly detection, and more using DynamoDB and its native vector search..."