LLM Routing Lab

Every LLM call has a cost — in dollars, in milliseconds, and in rate-limit headroom. Most applications default to one model for everything. That works until it doesn't: costs spike, latency climbs on simple requests, and a single model outage takes down the whole application.

A router changes the model selection from a hard-coded choice into a runtime decision. Send a one-sentence lookup to a general-purpose model. Send a deep analytical question to a reasoning model. Send a debugging request to a coding model. Retry on another tier automatically if the first one fails. Your application code doesn't change — only the router config does.

This lab will walk you through the concepts and how to configure a router to perform these steps. 

By the end of this lab, you will be able to:

1. Configure LiteLLM as a routing proxy with three LLM model backends
2. Explain how a complexity-based routing rule evaluates a prompt and selects a model
3. Deploy a working LLM router that routes simple prompts to a general model, reasoning-heavy prompts to a reasoning model, and code-related prompts to a coding model
4. Configure a fallback chain so the router recovers automatically if a tier is unavailable
5. Demonstrate the cost and latency difference between model tiers using LiteLLM's response metrics