Routing LLM Traffic on AWS: How to Build a Cost-Optimized Multi-Model API Router

When engineering teams first integrate Generative AI into their products, they usually make a rational, but ultimately expensive, decision: they pick the smartest model available and send every single query to it. Using Claude 3 Opus or GPT-4o for everything is the fastest way to get to market. But as your user base grows, your inference costs will scale linearly or worse, exponentially, if your context windows are expanding. The reality of production AI is this: You don't need a PhD-level reasoning engine to summarize a 3-paragraph email. Claude 3 Haiku or Llama 3 can handle 80% of standard production workloads at a fraction of the cost and with much lower latency. To protect your startup's runway and optimize your cloud economics, you need to stop hardcoding a single LLM into your backend. Instead, you need to build a Multi-Model API Router. Here is how to architect a dynamic LLM router using Amazon API Gateway, AWS Lambda, and Amazon Bedrock to reduce your inference costs by up to 6