Per-customer AI cost attribution by request fingerprinting without proxy

Per-customer AI cost attribution by request fingerprinting without proxy targets a $4.8B = 800,000 software-enabled businesses × $6K ACV for per-customer AI cost attribution tooling total addressable market with medium saturation and a year-over-year growth rate of 30% YoY LLM adoption growth (industry synthesis from McKinsey and developer tool adoption trends, 2023-2024).

Key trends driving demand: LLM adoption — more product teams are embedding LLMs into workflows, increasing usage and making per-customer cost visibility urgent.; Privacy and compliance concern — companies prefer solutions that avoid routing raw user data through third-party proxies, creating demand for non-proxy approaches.; Observability convergence — developers expect the same tracing and attribution primitives for LLM calls that exist for other APIs, enabling integration opportunities.; Rising API costs — as token and model costs fluctuate, finance and product teams require accurate per-customer cost metrics to price features and control spend..