Reduce LLM inference cost & latency with KV-cache-aware serving

Reduce LLM inference cost & latency with KV-cache-aware serving targets a $20.0B = 40,000 enterprises x $500K ACV (annual spend on LLM inference infrastructure & optimization) total addressable market with low saturation and a year-over-year growth rate of 40%+ expected growth driven by LLM adoption and cloud AI services.

Key trends driving demand: LLM adoption explosion -- more apps move to real-time LLMs, increasing inference spend pressure and demand for optimizations.; Open-source serving innovation -- projects like vLLM and Triton accelerate performant custom stacks that can adopt caching layers quickly.; Hardware specialization -- GPU/ASIC upgrades and batching strategies make caching-aware serving more valuable to extract utilization gains.; Hybrid/edge deployment -- enterprises require efficient on-prem and edge inference, where caching yields larger relative cost savings..