Stop hidden LLM billing leaks — detect and fix three cost patterns

Executive Summary

Enterprises running production LLMs increasingly face "hidden billing leaks"—small design patterns that multiply token and model costs across thousands of calls—and this problem lands on ML platform, SRE, and finance teams who must control recurring spend. The opportunity is material: about 160,000 potential enterprise buyers and an addressable market of roughly $9.6B (assuming $60K ACV), so even a 1% reduction in spend is meaningful for large customers but hard to detect with current tooling. A focused product could detect and fix three high-impact cost patterns—unbounded context/token inflation, inefficient model routing (expensive models used for low-value requests), and redundant or duplicated calls—by instrumenting SDKs, ingesting billing and telemetry, and producing per-request cost attribution. Core features would be real-time alerts, automated routing or throttling policies, prescriptive suggestions (model, temperature, context size), one-click fixes, and integrations for billing and policy-as-code enforcement. The timing is right: widespread LLM adoption, model proliferation, and the expectation that LLMs get the same observability as cloud infra create immediate demand, which is reflected in a market score of 88/100 and revenue potential of 90/100. To differentiate from medium-competition alternatives, the product must deliver low-friction instrumentation, vendor-agnostic model routing, and direct dollar-saved reporting as its primary KPI; success will still require solving integration, security, and enterprise-sales challenges through strong developer UX and a few initial anchor customers.