Amazon brings native real-time vector search to DynamoDB to support AI apps at scale

Amazon Web Services (AWS) has announced the general availability of vector search capabilities for DynamoDB, its high-availability NoSQL database. DynamoDB, launched in 2012, is known for its predictable performance and low operational overhead. Vector search, with single-digit millisecond latency and 99% recall capability, allows for semantic retrieval and supports any scale, including trillions of vectors.

This update enables DynamoDB to support large-scale applications requiring AI features such as retrieval-augmented generation, recommendation engines, personalization, and anomaly detection. Vector databases store high-dimensional data called embeddings, enabling fast similarity searches based on meaning rather than keywords. Many AI systems utilize vector databases for long-term memory and to handle large amounts of unstructured data, improving the accuracy of language models and preventing false outputs.

By integrating vector search into DynamoDB, developers can now utilize this capability alongside their operational data, leveraging the same managed serverless infrastructure and pay-per-request pricing. This move simplifies the process for developers, as they no longer need to manage separate storage and search systems. AWS currently offers native vector search capabilities through S3 Vectors, OpenSearch Service, and other specialized databases.

The update supports trillions of vectors and requires no server provisioning, patching, or management from developers, allowing them to focus on building AI-powered applications.