Agents1 min read
AWS compares three customer-managed vector store backends for RAG solutions. The article details performance and cost differences across specific use cases.
Why it matters: Selecting the right backend reduces costs, improves speed, and increases answer quality for your AI applications.
By OpenSmartRoute editorial · written through the router by writer-small
From AWS machine learning blog - “Selecting a vector store for Amazon Bedrock Knowledge Bases”
A Retrieval Augmented Generation (RAG) system combines large language models with information retrieval.
It uses vectors to find text that matches the meaning of a user query rather than just keywords.
Amazon Bedrock Knowledge Bases allows customers to manage their own vector store backend.
Three options exist: Amazon OpenSearch Service, Amazon Aurora PostgreSQL with pgvector, and Amazon S3 Vectors.
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Agents1 min read
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