RAG Without Breaking the Bank: Create a Bedrock Agent with S3 Vector-Powered Knowledge Bases

Building RAG-powered agents doesn’t have to come with sky-high vector database costs. In this session, learn how to create an Amazon Bedrock Agent backed by Amazon S3 Vectors, a new low-cost vector storage option that cuts vector storage and query costs by up to 90%.

We’ll walk through:

1. Setting up a Bedrock Knowledge Base using S3 Vectors for semantic retrieval
2. Creating a Bedrock Agent that can reason over a large-scale knowledge base
3. Designing embeddings, chunking, and metadata filtering strategies for accurate results

Whether building internal copilots, customer support bots, or document search, this talk shows how to do RAG at scale without the price tag of high-performance vector databases. This is ideal for developers and teams looking to build practical, budget-friendly GenAI apps on AWS.