Reduce ML Cost on Azure
Enterprise playbook to reduce GPU waste across AKS and Azure ML — with board-ready reporting.
Where Azure ML Waste Usually Hides
AKS GPU clusters
Idle GPU nodes, poor scheduling, and scale settings that keep capacity online when unused.
Azure Machine Learning runs
Repeated experiments, failed training loops, and artifact-less runs that still consume full cost.
Data pipeline bottlenecks
Slow reads and preprocessing stalls that keep GPUs waiting.
High-Impact Controls
- Stop failure loops: detect repeated OOM/failures and stop early.
- Deduplicate experiments: avoid redundant training with signatures.
- Right-size compute: allocate premium GPUs only where they change outcomes.
- Pipeline ownership: budgets and owners per pipeline reduce variance.
- Artifact enforcement: production pipelines must output artifacts or be flagged.
Azure-Specific Optimization Notes
- Autoscale discipline: scale up fast, but scale down reliably.
- Metadata consistency: unify run metadata so Finance + ML share the same narrative.
- Data locality: reduce I/O stalls that leave GPUs idle.
Verified Savings Model
You pay only 10% of verified savings. No savings → no payment.
Next Step
Start with a free ML cost audit to identify your top Azure waste drivers and quantify verified savings opportunities.