Cut LLM inference costs by routing repeated classification to cheap models

Cut LLM inference costs by routing repeated classification to cheap models targets a $1.2B = 60,000 AI-enabled SaaS and developer teams x $20,000 ACV. Assumes global companies that use LLM APIs at scale and would pay for inference spend optimization or orchestration at mid-market pricing. total addressable market with medium saturation and a year-over-year growth rate of 40%+ annual growth in LLM API spend among developer-first SaaS firms, driven by feature expansion and higher per-user inference rates.

Key trends driving demand: Rising LLM API spend -- drives demand for tools that reduce inference costs and improve unit economics.; Proliferation of small specialized models -- enables accurate, cheap local classifiers for many repetitive tasks.; Shift from prototyping to scale -- early LLM usage works at low volume, but production usage exposes cost and latency issues.; Developer-first buying -- dev teams prefer SDKs and orchestration layers that integrate into existing pipelines quickly..