Automatically route AI calls to lower‑cost models while preserving quality

Executive Summary

Many engineering and finance teams are watching API bills climb and juggling an increasing number of model options, yet lack a practical way to route calls to cheaper models without risking user-facing quality; this pain is widespread across an estimated 1,000,000 companies representing a $6.0B addressable market. Controlling per-call spend while preserving SLAs is a daily operational headache that slows down product velocity and inflates costs. You could build a developer-first SDK plus routing service that dynamically maps requests to lower-cost models when a lightweight quality estimator predicts parity, with real-time fallbacks, per-call attribution, and dashboarding to make savings and regressions visible. The product should be drop‑in (minimal code changes), support configurable quality thresholds, and include A/B testing and continuous evaluation to maintain trust. This is an attractive moment: model proliferation and rapidly rising AI bills create demand for cost-control tooling, and with a market score of 88/100 and revenue potential 86/100 there’s room to capture value from teams focused on infrastructure savings. The edge comes from combining measurable quality guarantees, fine-grained attribution, and a frictionless developer experience; primary challenges are building robust per-call quality estimators, keeping latency low, and integrating across vendors, but those are engineering problems with clear mitigation paths.