Selecting a vector store for Amazon Bedrock Knowledge Bases
Vector store choice can cut RAG latency by 60% — here's how to pick for Bedrock.
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.
The key facts
9 to knowCompares 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
Go to the source
AWS Machine Learning Blogaws.amazon.com
Publisher excerpt: 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…