# Model routing & cascades

> Cut My AI Spend — rank #3 of 10. Typical savings: 40–98% depending on workload mix. Effort: Medium — needs routing logic and evals.

Most requests do not need your most expensive model. Routing sends simple queries to cheap models (Haiku, GPT-mini class, Nova) and reserves frontier models for the requests that actually need them; cascade designs try cheap first and escalate only on failure.
Stanford FrugalGPT-style cascade research demonstrated up to 98% cost reduction at matched quality, and production teams routinely report 40–70% by classifying request complexity up front. The catch: you need a way to decide (a classifier, heuristics, or confidence checks) and evals to prove quality held.

## How to do it

1. Segment traffic by task type; label which segments a small model already handles well.
2. Add a router: heuristic rules, a tiny classifier model, or an LLM gateway with built-in routing.
3. For cascades, define an acceptance check (schema validity, confidence, judge model) that triggers escalation.
4. Run A/B evals per segment before and after; watch for silent quality regressions.

## Frequently asked questions

### What is the difference between routing and a cascade?

Routing decides the model before the call based on the request. A cascade calls the cheap model first and escalates to a stronger one only when the answer fails a check. Cascades save more but add latency on escalated requests.


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