Model Routing › Summary

Model routing is the practice of directing inference requests to the most appropriate model based on estimated difficulty, cost constraints, and quality requirements. Key takeaways:

• Routing strategies range from deterministic rules (token length thresholds, user tiers) to learned classifiers that predict request difficulty from surface features, with cost-aware formulations that make the quality-cost tradeoff explicit and tunable.
• Model cascades implement a sequential approach: try the cheap model first, evaluate confidence, and escalate to a more capable model only when necessary. The confidence threshold controls the cost-quality tradeoff. The escalation-quality curve typically shows diminishing returns beyond 30-40% escalation rate.
• A/B testing is essential for validating routing decisions. Compare routing strategies with statistical rigor using appropriate sample sizes, significance thresholds, and guardrails against harmful treatments. Never trust a routing improvement without measuring it.
• Cold start is the primary operational challenge: learned routers require labeled routing outcome data that only exists after the system has been running. Bootstrapping through shadow evaluation or rule-based initialization is necessary.
• Routing overhead must be small relative to the small model's latency. Routers that are expensive to run negate their own savings. Practical classifiers operate in under 5ms.
• Online learning keeps routers up to date as request distributions shift, but requires careful regularization to avoid instability from short-term fluctuations.
• Observability is what separates routing systems you trust from ones you fear. Monitor escalation rate trends, per-tier quality distributions, and router accuracy on labeled holdout sets continuously.

Production routing systems demonstrate that significant cost savings are achievable without sacrificing quality, provided the routing decision is well-calibrated and continuously monitored. The engineering investment in building, calibrating, and maintaining a routing system pays for itself many times over at any meaningful scale.