Amazon Bedrock has evolved into a full AI platform on AWS, covering foundation models from 18+ providers, managed RAG via Knowledge Bases, agent runtimes (classic Agents and the newer AgentCore), guardrails, fine-tuning, and observability. The guide covers when to choose Bedrock over direct provider APIs or self-hosting, how to use the Converse API as the default inference surface, RAG chunking strategies and vector store trade-offs, the difference between classic Bedrock Agents and AgentCore (microVM-based, framework-agnostic, MCP-compatible), cost management patterns including batch inference and prompt caching, and a 10-point decision framework for new projects. Key recommendations: default to Converse API, use Knowledge Bases unless retrieval is a core product feature, adopt AgentCore for production agents, add Guardrails from day one, pin model versions, and build evaluation harnesses before shipping.
