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

Cut LLM inference 95% by routing uncertain cases to frontier models targets a $12.0B = 60,000 AI-heavy software businesses x $200K ACV. Assumes mid-market and enterprise SaaS teams that run large LLM workloads and pay for inference and orchestration. total addressable market with medium saturation and a year-over-year growth rate of 40% yearly growth in enterprise LLM API spend as AI features proliferate in SaaS.

Key trends driving demand: LLM cost pressure -- as more products add AI, per-call inference costs drive architecture changes and demand for optimization tools.; Hybrid model stacks -- emergence of small classifiers, distilled models and embeddings to handle deterministic work at lower cost.; Developer-first AI tooling -- frameworks and infra (chains, vector DBs, caches) make orchestration and routing feasible in production.; E-commerce automation growth -- catalog matching, moderation and enrichment drive continuous high-volume inference workloads..