Cut LLM inference 95% by routing uncertain cases to frontier models

Executive Summary

High-volume AI SaaS faces exploding inference bills. Solve it by classifying repetitive cases with cheap models/rules and only sending uncertain inputs to expensive LLMs, restoring SaaS-like margins. LLM usage is moving from prototyping to production, creating sharp inference cost pressure as usage multiplies. The source reports that shipping with an LLM-first approach is fast early but unsustainable as volume grows. At the same time, practical building blocks are available now - cheap distilled or open models, fast vector stores, confidence estimation techniques, and mature caching layers - making conditional routing feasible. Rising API prices and enterprise focus on margin recovery make this an immediate pain for developer-led SaaS teams. Built specifically for high-volume AI SaaS workflows by combining cheap deterministic checks, small specialized classifiers, and a confidence-based router that only escalates uncertain cases to frontier models. Evidence from the source shows a product scanning large e-commerce catalogs cut 95 percent of LLM calls by identifying repeated classification work and only sending uncertain cases to the expensive model, which directly targets the core economic failure mode of LLM-first designs.