How to Cut Your LLM Classification Costs by 90%

A clear technical walkthrough of model cascades: route classification requests to a cheap model first and escalate to an expensive one only when confidence is low, with a concrete threshold-finding algorithm you can implement this week. Honest about limits too — it only works where confidence is calibrated (finite label sets), and the method fails loudly rather than silently when confidence scores are uninformative.

• Sample 200-500 items, run both proxy and oracle, then simulate thresholds to find the minimum-cost point that hits your target accuracy (e.g., 95% agreement).
• This works for classification with calibrated confidence, not open-ended generation where confidence scores are unreliable.
• If confidence is uncorrelated with accuracy, no threshold will meet your target — the method self-diagnoses rather than lying to you.
• For statistical guarantees beyond the sample-based estimate, use concentration inequalities or the Bargain Python package.

Part of Issue Nº 005: How Unify cut agent costs 95%, and why RL-trained agents break in the wild