Building cost-effective RAG applications with Amazon Bedrock Knowledge Bases and Amazon S3 Vectors
Vector embeddings have become essential for modern Retrieval Augmented Generation (RAG) applications, but organizations face significant cost challenges as they scale. As knowledge bases grow and require more granular embeddings, many vector databases that rely on high-performance storage such as SSDs or in-memory solutions become prohibitively expensive. This cost barrier often forces organizations to limit…
