Cut LLM inference costs 95% with uncertainty-based filtering

Cut LLM inference costs 95% with uncertainty-based filtering targets a $6.0B = 30,000 AI-enabled SaaS companies x $20,000 ACV. Assumes a broad population of mid-market SaaS vendors embedding LLM features who would pay for ongoing inference optimization and orchestration services. total addressable market with medium saturation and a year-over-year growth rate of 35% yearly growth in AI feature adoption among SaaS vendors, increasing inference spend pressure.

Key trends driving demand: Open and efficient LLMs -- cheaper local inference options make hybrid routing viable and reduce dependence on frontier models.; Rising API prices -- larger vendors experimenting with price increases or tiered pricing, creating urgency to optimize calls.; SaaS margin pressure -- customers measure gross margins more tightly as inference becomes a material COGS line.; Workflow-heavy AI features -- tasks like catalog scanning, moderation, and compliance are high volume and repeatable, ideal for filtering.; Improved uncertainty estimation -- model confidence and calibration techniques let systems safely gate calls to expensive models..