Cut LLM inference costs 95% with uncertainty-based filtering

Executive Summary

High LLM bills are killing SaaS margins. Use deterministic classifiers, caching and uncertainty routing so only borderline cases go to expensive frontier models, cutting calls and restoring SaaS-like economics. LLM economics have shifted - early prototyping used blanket API calls, but at scale inference spend dominates SaaS margins. The source shows real usage in ecom compliance where repeated classification was the culprit. Two concrete enablers make this timely: availability of open models and on-prem/local inference for cheaper routing, and mature methods for uncertainty estimation and confidence thresholds that let systems safely filter calls. Meanwhile regulatory and compliance workloads magnify cost sensitivity because they are high volume and recurring. Position as an inference orchestration layer that combines deterministic classifiers, cached decisions, local or cheaper models, and uncertainty scoring to route only ambiguous cases to frontier LLMs. The Reddit example shows a concrete win - a compliance product scanning large ecom catalogs cut 95 percent of calls by recognizing repeated classification work and only sending uncertain cases upstream. That practical workflow focus - catalog scanning, extraction verification, routing decisions - makes the product a workflow-first optimizer rather than a generic model host, enabling fast time to value for developer teams already paying high inference bills.