FinOps✓ Mathematical
◆ The PatternTracking cost per prediction and eliminating ML waste
ML workloads are expensive — GPUs, storage, compute for training and serving. Cost governance tracks cost per prediction, GPU utilisation, idle resources, and spot vs reserved savings. The goal: same model quality at lower cost, or better models at the same cost.
// Interactive — cost breakdown by category
| Strategy | Savings | Risk |
|---|---|---|
| Spot/preemptible instances | 60–90% | Interruption risk |
| Right-sizing instances | 20–50% | Under-provisioning |
| Model compression | 40–75% | Accuracy loss |
| Prediction caching | 50–80% | Stale results |
Pattern bridge: Cost governance applies the same risk-adjusted return thinking to infrastructure — maximise model value per dollar spent, just as transaction cost analysis measures trading efficiency.