LLM Routing Explained: Cut API Costs 50% Without Losing Quality
LLM routing sends each request to the right-sized model. We explain the strategies, when to use them, and real cost numbers.
💡 What You Will Learn
LLM routing sends each request to the right-sized model. We explain the strategies, when to use them, and real cost numbers.
📜 Table of Contents
The core insight of LLM routing is embarrassingly simple: not every prompt needs a frontier model. Summaries, classification, extraction, and code completion for common patterns are handled fine by small models - and small models cost 10-100x less.
Routing Strategies
Rule-based: map known tasks to fixed models (e.g. extraction goes to a 7B model, complex reasoning goes to frontier). Cheap, transparent, zero risk. Learned routing: train a classifier on preference data like RouteLLM (5,309 stars) does - it learns the difficulty boundary automatically. Cascade: try the cheap model first, escalate to the big model only if confidence is low.
Real numbers: a team routing 2M requests/month between DeepSeek-class and GPT-class models typically cuts the bill from thousands of dollars to a few hundred. The quality check is simple - sample 1 percent of routed traffic weekly and have a human or strong model judge the answers.
Comparison
| Strategy | Complexity | Risk |
|---|---|---|
| Rule-based | Low | Lowest |
| Learned routing | Medium | Medium |
| Cascade | Medium | Low |
FAQ
Q: Which queries should NEVER be routed?
A: Medical, legal, and financial advice, and anything where a wrong answer has real cost. Keep those on the best model.
Q: How do I measure quality after routing?
A: Sample 1 percent of traffic weekly, judge with a strong model or human, and track a quality score alongside the cost savings.
Written by our editorial team; tools listed here are tested or verified against public sources. Links point to official sites or GitHub repos for reference only โ no paid placements.
