Cut LLM bills by routing uncertain cases to frontier models

Executive Summary

High-volume AI products hit razor-thin margins because they send every request to expensive LLMs. Use cheap classifiers, caching, and an uncertainty-first model cascade so only ambiguous cases reach the costly model, restoring SaaS-like margins. LLM usage explosion and high inference costs are creating a new operational pain point for AI-heavy SaaS, as described by the founder who faced ugly inference economics when scanning large ecom catalogs. At the same time, cheaper and performant small models, open weights, and efficient inference tooling make cascades and partial local inference feasible. Increased regulatory and compliance scrutiny in verticals like marketplaces and content moderation raises the cost of errors, so selective routing to higher-trust frontier models only for uncertain cases is now a practical way to manage both cost and risk. Exploit model cascades and uncertainty estimation to convert repeated LLM calls into inexpensive deterministic or small-model work, then call a frontier model only for uncertain cases. The source shows a founder cutting 95% of LLM calls by identifying repeated classification work and only sending edge cases to the large model, which both preserves quality and restores SaaS-like margins. Positioning combines engineering patterns (caching, classifier + rerank, thresholds), domain-specific feature stores for product catalogs, and per-customer labeled data that grows with usage to improve classifiers and reduce frontier calls.