LLM spend leakage — detect, attribute & reduce API cost waste

Many engineering and finance teams at organizations running production LLMs are facing rapidly rising and poorly understood API bills: the addressable market is roughly 200,000 orgs and a $4.5B opportunity at an average $22.5K ACV for a FinOps/observability offering. Developers, SREs, ML-platform teams and CFOs struggle with cross-provider complexity (OpenAI, Anthropic, Google Cloud, Azure), untagged call-level spend, and feature-level cost attribution that makes cost ownership and optimization difficult. You could build a developer-focused FinOps platform that detects spend leakage, attributes cost to code paths/features/users across multiple providers, and reduces waste via automated anomaly detection, prompt and model recommendations, and programmable guardrails. Deliver both lightweight SDK instrumentation and agentless billing-correlators, plus integrations into existing observability and chargeback systems so teams can map cost to business metrics and recoverable savings. This market is compelling now because LLM adoption is accelerating across industries, multi-provider strategies are becoming standard, and finance leaders want the same ROI visibility they have for cloud spend; market and internal scoring suggest a 95/100 market score and 90/100 revenue potential. With 200,000 potential customers and accelerating per-seat model bills, there is a narrow window to capture customers before observability incumbents adapt. To stand out you’ll need rigorous cross-provider attribution, low-friction developer ergonomics, and proof points showing double-digit percentage savings in pilots (e.g., 15–40% potential reductions), but expect real challenges in instrumentation fidelity, privacy/legal constraints with request payloads, and competition from both cloud vendors and observability platforms — success will hinge on fast ROI in pilot customers and defensible integrations.