Amazon DynamoDB now supports real-time vector search at any scale

Amazon has taken another significant step toward simplifying AI application development by introducing real-time vector search in DynamoDB. This enhancement allows developers to store operational data and AI embeddings in the same database, eliminating the need to maintain a separate vector database for many use cases.

This is particularly useful for applications powered by Retrieval-Augmented Generation (RAG), semantic search, recommendation systems, fraud detection, and personalized user experiences. With native vector similarity search, applications can retrieve the most relevant information in milliseconds while continuing to benefit from DynamoDB's high availability, low latency, and virtually unlimited scalability.

For teams already using DynamoDB, this feature reduces architectural complexity by keeping transactional data and vector embeddings together. Instead of synchronizing data between multiple services, developers can build AI-powered applications on a single, managed platform, reducing both operational overhead and infrastructure costs.

How to use DynamoDB Vector Search

Getting started with DynamoDB's vector search is straightforward:

• Create a DynamoDB table to store your application data.
• Generate vector embeddings using an embedding model such as Amazon Titan Embeddings, OpenAI, Cohere, or another supported model.
• Store the embedding alongside your application record in DynamoDB.
• Create a vector index on the embedding attribute to enable similarity search.
• Convert the user's query into an embedding using the same embedding model.
• Run a vector search by comparing the query embedding with stored embeddings to retrieve the most similar results in real time.
• Use the results in your application—for example:
  • Provide relevant context to an LLM in a RAG workflow.
  • Recommend similar products or content.
  • Perform semantic document search.
  • Detect similar fraud patterns or anomalies.

Example use case

Imagine you're building an AI-powered customer support chatbot. Instead of searching documents by keywords, you:

• Generate embeddings for every support article.
• Store those embeddings in DynamoDB.
• Convert a customer's question into an embedding.
• Perform a vector similarity search.
• Return the most relevant articles to the LLM, enabling more accurate and context-aware responses.

Why this matters

By bringing vector search directly into DynamoDB, AWS enables developers to build intelligent applications without introducing another specialized database into their architecture. This simplifies development, reduces operational complexity, and makes it easier to scale AI workloads alongside traditional application data.

As AI adoption accelerates, integrating vector search into a widely used NoSQL database reflects a broader industry shift toward unified data platforms that support both transactional workloads and AI-driven search. For many organizations, DynamoDB's native vector search will be enough to power production-ready AI applications while leveraging the reliability and scalability they already trust.