Prevent runaway GPU/cloud compute spend by autoscheduling + cost controls targets a $60.0B = 30,000 large enterprises x $2.0M avg annual AI/accelerated compute spend (cloud + on-prem) where tooling & optimization could capture ~5%-10% of spend total addressable market with medium saturation and a year-over-year growth rate of 35%+ (AI/ML infra spend and GPU cloud consumption growth).

Key trends driving demand: GPU commoditization -- cheaper, widely available GPUs increase volume of experiments and accidental spend; Team-level compute access -- self-serve infra means more ad-hoc jobs and less centralized cost discipline; Observability + AI -- improved telemetry and forecasting models make per-job cost prediction viable; Kubernetes & cluster managers convergence -- standard orchestration layers enable agent-based enforcement across environments.