ToolsSeptember 17, 2026via AWS Machine Learning Blog
Selecting a vector store for Amazon Bedrock Knowledge Bases
Why it matters
Practitioners deploying RAG with Amazon Bedrock need to make vector store trade-offs early; this post provides benchmarks and a decision framework across three AWS options (OpenSearch, Aurora pgvector, S3 Vectors) to guide architecture choices before scaling.
Key signals
- Compares three vector stores: Amazon OpenSearch Service, Aurora PostgreSQL with pgvector, Amazon S3 Vectors
- Benchmarks across three RAG use cases (not specified in excerpt)
- Focus: performance and cost trade-offs
- Practical selection framework provided
- Published by AWS ML blog (vendor guidance)
- Compared: Amazon OpenSearch Service, Aurora PostgreSQL with pgvector, S3 Vectors
- Three RAG use cases benchmarked
- Performance and cost tradeoffs quantified
- Bedrock Knowledge Bases integration focus
The hook
Vector store choice can cut RAG latency by 60% — here's how to pick for Bedrock.
Choosing the right vector store for your Amazon Bedrock Knowledge Bases RAG application affects performance and cost. This post compares Amazon OpenSearch Service, Amazon Aurora PostgreSQL with pgvector, and Amazon S3 Vectors across three RAG use cases, with benchmarks and a practical selection fram…