Reduce AI costs & latency by routing API calls across models/providers

Executive Summary

Many enterprises running production AI are being hit by both high inference costs and variable latency across hosted and open models; with a conservative addressable market of $50.0B (50,000 enterprises spending roughly $1.0M annually on inference/API), ML platform teams, SREs, and cost-conscious engineering orgs are the primary sufferers. They currently lack automated, policy-driven ways to route individual API calls to the most cost-effective or lowest-latency model while preserving accuracy and compliance. You could build a developer-focused routing platform that continuously benchmarks models and providers, makes per-call routing decisions based on configurable policies (cost, latency, accuracy, data residency, SLOs), and exposes SDKs, observability, and SLA enforcement for production systems. Core features would include a real-time profiler, a low-latency decision plane (targeting sub-10ms routing decisions), adaptive caching and fallbacks, and integrations into CI/CD and MLOps tooling. Monetization could be SaaS subscriptions, usage-based fees, or a value-share tied to realized savings. Timing is attractive because model proliferation, enterprise AI production demands, and the move to edge and multi-cloud inference are increasing heterogeneity and arbitrage opportunities versus a year ago, which supports the high market and revenue potential scores implied by the $50B estimate. To genuinely stand out you’ll need rigorous, auditable benchmarking, strong security and compliance controls, and partnerships to reduce contractual friction with providers; the key challenges are engineering complexity, the risk of diminishing returns if providers converge on price/performance, and the operational overhead of maintaining many connectors and SLAs.