Measure ML training carbon footprints and export audit-ready reports

Measure ML training carbon footprints and export audit-ready reports targets a $7.2B = 120,000 organizations (enterprises + research labs) x $60K ACV total addressable market with medium saturation and a year-over-year growth rate of 23% CAGR in enterprise sustainability software and ML tooling.

Key trends driving demand: Regulation & ESG reporting -- increasing regulatory pressure and investor demands require traceable emissions reporting including cloud compute, creating a market for ML-specific carbon tools.; Cloud-native ML ops -- richer telemetry from frameworks (PyTorch, TensorFlow) and cloud APIs makes non-invasive energy estimation accurate enough for reporting.; Cost sensitivity of model training -- rising cloud/GPU costs force teams to account for energy and emissions as part of optimization and budgeting.; Open-source standardization -- community-driven tools and formats (open-source energy trackers) are creating de facto standards for instrumentation and export..