AWS Guide: Route LLM Requests Dynamically Using AppConfig Without Redeploying Code

Developers using Amazon Bedrock face frequent disruptions as new AI model versions from Anthropic, Amazon, and Meta release every few months, often requiring code changes and redeployments to switch models. A new hands-on tutorial demonstrates how to decouple model selection from application code by storing routing logic in AWS AppConfig feature flags. In this architecture, an API Gateway and Lambda function read configuration at runtime, directing requests labeled 'fast', 'cheap', or 'open' to Claude Haiku, Nova Micro, or Llama respectively. Because the model ID lives in AppConfig rather than in code, swapping or rolling back a model requires only a configuration deployment with no redeploy needed. AWS AppConfig supports versioned history, gradual rollout strategies, and efficient runtime polling, making it well-suited for managing live LLM routing in production environments.