Hidden LLM Spend — detect token waste & enforce cheaper flows

Executive Summary

Many engineering organizations and product teams face opaque, unnecessary LLM API spend—estimated $50B annual global LLM API spend—and token usage is fragmented across services, pipelines, and microservices. This problem is most acute in mid-to-large tech companies, platform teams, and ML-heavy startups that run many models and versions in production and lack tooling to attribute token costs to specific codepaths or features. You could build a developer-focused observability and enforcement platform that maps token usage to commits, endpoints, and feature flags, detects anomalous or duplicated tokens, and enforces cheaper routing (for example, fallbacks to smaller models, cached responses, or pre- and post-processing to reduce tokens) via SDKs and API proxies. Deliver dashboards and per-feature cost allocation, automated policy rules, and lightweight agents that add under 1–3 ms latency while enabling per-request cost attribution and automated remediation. The timing is favorable: model proliferation and pay-as-you-go pricing have made token waste directly impactful—1% inefficiency on a $50B market already equals $500M of annual waste—and teams are already investing in ML observability, reducing adoption friction. Market indicators (high market and revenue scores) and a medium-competitive landscape suggest a clear window to capture value with a specialized tool. To stand out, prioritize developer ergonomics (simple SDKs, Git and CI/CD integrations), high-fidelity attribution through sampling and lightweight tracing, and a neutral policy engine that supports multi-cloud and multi-model routing rather than becoming a model vendor. Be honest about challenges: instrumenting diverse stacks, payload privacy/security constraints, and the risk that major cloud or model vendors will add overlapping features, which means success will likely require strong integrations and partner channels rather than a pure product-led breakout.