Per-customer AI cost attribution by request fingerprinting without proxy

Product and finance teams at software-enabled businesses embedding LLMs increasingly lack reliable per-customer cost visibility: LLM calls are token-priced, high-volume, and often multiplexed across tenants, which prevents accurate showback, chargeback, and cost optimization. The addressable market is large—roughly 800,000 potential customers willing to pay about $6K ACV for per-customer AI cost attribution tooling—implying a $4.8B opportunity. You could build a developer-first platform that fingerprints requests at the SDK or sidecar layer using deterministic, privacy-preserving hashes of non-sensitive request features, aggregates cost attribution server-side, and exports per-customer metrics into billing and observability pipelines (OpenTelemetry, BI, payments). By design it would avoid routing raw user content through a third-party proxy, ship dashboards and billing exports, and provide lightweight SDKs for common stacks to reduce integration friction. This market is attractive now because LLM adoption is accelerating, customers are wary of proxying sensitive user data for compliance reasons, and engineers expect the same tracing and attribution primitives for LLM calls that exist for other APIs; our market and revenue scores (88/100 and 82/100) reflect that. Teams facing five‑figure monthly LLM bills have a clear incentive to pay for tooling that reliably apportions cost and exposes regressions. To differentiate you must prove high attribution fidelity, publish threat and privacy models, and integrate deeply with existing observability and billing systems while offering a low-friction SDK-first experience and open-source collectors to build trust. The main challenges are real: keeping accuracy as prompts and models evolve, handling anonymized or adversarial inputs, and achieving legally defensible privacy guarantees—overcoming those will require rigorous validation, SLAs, and strategic partnerships with LLM and cloud vendors.