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

Enterprises building real-time LLM applications — assistants, RAG pipelines, and high-throughput chat services — are paying hundreds of thousands of dollars a year to run inference and repeatedly recomputing transformer states because serving stacks don’t treat the KV cache as a first-class, sharded, network-aware resource. With an estimated addressable market of 40,000 enterprises spending roughly $500K each (a $20B market), this inefficiency directly hits both latency-sensitive UX and enterprise cost lines. You could build a KV-cache-aware serving layer that integrates with vLLM, Triton, and common orchestrators to expose cache-aware batching, cross-request reuse, eviction/placement policies, and telemetry for per-request hit rates and cost attribution. Bundled SDKs and enterprise features (multi-tenancy, SLOs, secure cache isolation) would let customers drop it into existing stacks and measure end-to-end reductions in GPU compute and tail latency — conservatively 20–60% improvements depending on hit rate and context reuse patterns. Timing favors this product: open-source serving innovation and more specialized GPU/ASIC strategies make it practical to change the serving plane, and the LLM adoption wave means many teams face acute inference spend pressure now. Competition is currently low, so early technical and commercial wins could capture significant wallet share if you move quickly. To stand out, prioritize engineering depth (sharded-cache placement, hardware-aware batching), rigorous, reproducible benchmarks, and enterprise reliability; the principal challenges are the engineering complexity of supporting diverse model runtimes, the variability of cache hit rates across workloads, and the need to continuously adapt as model internals and inference optimizations evolve.