Engineering and product teams — plus emerging FinOps owners — at the roughly 2.0M businesses adopting LLMs face fragmented visibility and predictable waste in API spend; with a $4.8B addressable market and an average customer ACV of $2.4K, even a conservative 10–30% avoidable spend represents hundreds of millions of dollars in opportunity. Teams commonly lack per-endpoint token accounting, model- and prompt-level cost attribution, and automated controls (routing, caching, batching) that prevent routine overruns. Build a visibility + optimization dashboard that ingests billing data and request telemetry to provide per-request token breakdowns, model-cost simulations, rule-based routing to cheaper or open-source models, prompt batching/caching libraries, and automated recommendations with estimated dollar savings. Ship language SDKs (Python/Node), CI integrations for cost regression tests, real-time alerts for outliers, and a policy engine to enforce budgets and model-substitution thresholds. This market is attractive now because LLM proliferation, model diversity, and the rise of AI FinOps make cost optimization both possible and a native priority; the market score of 90/100 and the $4.8B TAM reflect strong demand for tooling that turns usage into predictable, controllable spend. You can differentiate by instrumenting at the SDK level to capture actionable telemetry, combining econometric savings estimates with developer-facing controls (IDE hints, CI gates), and focusing sales on measurable ROI targets (15–30% savings). Expect challenges in instrumenting heterogeneous stacks, preserving privacy when collecting telemetry, integrating with multiple model providers, and proving value in early pilots, but current low competition and clear unit economics make a focused product worth pursuing.