Opaque LLM token billing — precise token accounting & cost controls

Engineering, product and finance teams at roughly 1.5M global software and product teams face opaque LLM token billing that leads to surprise invoices, poor feature-level attribution and limited ability to enforce cost controls. As LLM usage fragments across backend services, client SDKs and edge agents, teams report material variances in expected versus billed token usage and struggle to tie spend to features or users. You could build a developer platform that delivers precise token accounting and cost controls through lightweight proxy SDKs, per-request token attribution, model-tokenizer reconciliation, policy enforcement, alerts and direct billing integrations, with first-class support for both public APIs and private/on‑prem endpoints. Target reconciliation accuracy within 1–3% for supported model families while being transparent that provider-side tokenization, runtime truncation and emerging model formats will require continuous updates and selective vendor cooperation. This is an attractive moment: a $12.0B market (1.5M teams × $8K ACV), with a market score of 92/100 and revenue potential scored 88/100, reflects sizeable demand for AI cost observability as tokenized pricing becomes the norm. The proliferation of LLM-powered product features and the shift toward hybrid and private models are increasing distributed token burn and creating urgency for attribution and control tools. To stand out, prioritize hybrid enterprise support, deep reconciliation with finance systems and low-friction SDKs that enable feature- and user-level chargebacks rather than only aggregate metrics. Strengths are clear product-market fit and measurable ROI; challenges include keeping pace with many model tokenizers, achieving near-perfect parity with provider bills and persuading engineering teams to adopt instrumentation—this is worth pursuing but will require 12–18 months of focused engineering and partnership work to validate at scale.