Cut LLM waste — auto-select and route calls to the cheapest accurate model

Executive Summary

Many engineering teams are quietly losing material margins to LLM API spend: taken together the addressable market is about $12.0B (5,000,000 engineering teams spending an average of $2,400 annually), and teams from startups to large product orgs report unpredictable bills and hard-to-measure model quality. The result is a twofold problem — per-call cost variability across dozens of models and providers, and the operational burden of continuously measuring accuracy so that cheaper models do not degrade product behavior. You could build an automated routing layer that selects and routes each inference call to the cheapest model that meets an accuracy threshold, combining real-time cost/latency signals, per-tenant quality profiles, and continuous A/B evaluation. This is attractive now because model fragmentation creates clear arbitrage opportunities, enterprise LLM spend is rising rapidly, and MLOps commoditization (observability, feature flags, CI/CD) lowers integration friction; our conservative scores reflect that potential (Market Score 95/100, Revenue Potential 92/100) while competition is medium and fragmented. To stand out you must be rigorous about lived accuracy measurement, have low-latency routing, and solve data governance so enterprises can meet compliance while realizing savings (conservative early customers should expect 20–40% cost reductions on multi-model stacks). The honest challenges are nontrivial: building robust ML evaluators, handling provider pricing churn, proving accuracy across diverse prompts, and integrating into existing pipelines — but if solved, the ROI and adoption path are direct and measurable.