Reduce LLM API spend via caching, model-switching, and hybrid inference

Reduce LLM API spend via caching, model-switching, and hybrid inference targets a $36.0B = 600,000 software companies x $60K annual LLM/API spend (total addressable spend on inference & API calls) total addressable market with medium saturation and a year-over-year growth rate of 35-50% annual growth in API/inference spend as adoption accelerates.

Key trends driving demand: Model proliferation -- multiple competing model families (open-source and hosted) create opportunities to route to cheaper models where quality is sufficient.; Hybrid on-prem + cloud inference -- enterprises adopt mixed inference to balance privacy and cost, enabling tools that orchestrate both.; Observability & MLOps maturity -- teams expect tooling to measure latency, cost, and quality, which enables automated optimization.; Vector/cached retrieval growth -- increasing use of retrieval means many queries can be answered from cache or vectors instead of full LLM calls..