Selecting a vector store for Amazon Bedrock Knowledge Bases | Amazon Web Services

When building a Retrieval Augmented Generation (RAG) solution with Amazon Bedrock Knowledge Bases, selecting the right vector store impacts performance and cost. Amazon Bedrock Knowledge Bases offers a fully managed option and a customer-managed option where you choose your own vector store. This post focuses on the customer-managed path, comparing the three supported backends: Amazon OpenSearch Service, Amazon Aurora PostgreSQL with pgvector, and Amazon S3 Vectors, a capability of Amazon Simple Storage Service (Amazon S3), across distinct RAG use cases.

For broader guidance across all AWS vector solutions, see AWS vector solutions: Build agentic AI where your data lives. For the role of vector datastores in generative AI applications, see The role of vector datastores in generative AI applications. For prescriptive guidance on vector databases for RAG, see Choosing an AWS vector database for RAG use cases.

How vector databases fit into RAG...

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