Reduce LLM API Spend with Dynamic Model Routing & Caching

Reduce LLM API Spend with Dynamic Model Routing & Caching targets a $24.0B = 400,000 companies x $60K ACV (enterprise & mid-market AI infra spend for LLM APIs & orchestration) total addressable market with medium saturation and a year-over-year growth rate of 30% = projected annual growth in LLM infrastructure and API spend as LLMs expand into more apps.

Key trends driving demand: Multi-model availability -- providers and open-source models create choice but inconsistent cost/perf tradeoffs, enabling routing arbitrage.; Observability-first AI development -- teams demand latency, cost, and prompt observability, which routers can centralize.; Edge and self-hosted models -- cheaper local inference options create opportunities for hybrid routing (cloud + on-prem).; Caching & memoization -- deterministic or high-recall tasks are increasingly cached to reduce token consumption..