Amazon Bedrock: How to Choose the Right Vector Store for RAG

It is the component that stores and indexes document embeddings so a RAG application can retrieve the most relevant chunks before passing them to the language model. Bedrock supports options including OpenSearch, Aurora PostgreSQL with pgvector, and S3 Vectors.

OpenSearch is a search engine tuned for hybrid search, filtering, and concurrent queries. Aurora PostgreSQL with pgvector keeps vectors alongside relational data in one database, reducing system count. S3 Vectors lowers cost at scale but is less suited to low-latency concurrent queries.

Yes, especially teams building internal assistants or knowledge search on AWS. Picking the wrong vector store early leads to costly re-engineering later, and the framework reduces that risk.

Query pattern comes first: do you need hybrid search with complex filters, integration with relational data, or large-scale low-frequency retrieval? Then consider data volume, required latency, and cost.