Hands-On Lab Workshop: LLM Routing Lab
Event Overview
Every LLM call comes with a cost — in dollars, in milliseconds, and in rate-limit headroom. Most applications default to a single model for everything, and that works until it doesn't: costs spike, latency climbs on simple requests, and a single model outage can take down the whole application. In this session, we'll introduce LLM routing — turning model selection from a hard-coded choice into a runtime decision, so a quick lookup goes to a general-purpose model, a deep analytical question goes to a reasoning model, and a debugging request goes to a coding model, with automatic retries across tiers if one fails. Join us for a hands-on walkthrough of the concepts and a live look at configuring a router to put these ideas into practice.
Featured Speakers
What to expect
- A live walkthrough of the LLM Routing Lab hands-on environment.
- An introduction to LiteLLM as a routing proxy across multiple model backends.
- A demonstration of complexity-based routing rules in action.
- A look at real cost and latency differences across model tiers.
- A discussion of fallback and failover strategies for model outages.
- Live Q&A with presenter Chris Nugent.
Goals and Objectives
• Configure LiteLLM as a routing proxy with three LLM model backends • Explain how a complexity-based routing rule evaluates a prompt and selects a model • 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 • Configure a fallback chain so the router recovers automatically if a tier is unavailable • Demonstrate the cost and latency difference between model tiers using LiteLLM's response metrics
Who should attend?
This Lab Workshop will be valuable to AI Infrastructure Engineers, AI Practitioners, AI for Business Leaders and Developers building AI Assistants and Agents. This session is a great fit for engineers and architects building agentic AI applications who want to reduce cost and latency while improving reliability across multi-model systems.