AI FinOps
GPU invoices mix reserved training capacity, burst inference, idle nodes and data-egress to storage or evaluation clusters. Generic cost allocation hides that mix. This engagement separates those unit costs.
Who this engagement is for
Platform and ML engineering leads who already run GPU or managed inference and need a unit-cost model, not a generic FinOps slide.
Information required to begin
- GPU SKUs, regions and reservation or committed-use contracts
- Training versus inference split and typical job duration
- Idle-time observations from the last 30 days if available
- Data movement paths (checkpoints, evaluation, logging)
Engineering process
- Consultation to separate training, inference and shared storage spend
- Review of instance reservations, committed use and idle waste
- Written unit-cost model and governance recommendations
Deliverables
- AI workload unit-cost model
- Cost anomaly findings for GPU and related egress
- Governance notes for reservation vs on-demand decisions
Provider-specific scope
- AWS EC2, SageMaker and Trainium/Inferentia SKUs as used in your accounts
- Azure ND-series, ML compute and reservations
- Google Cloud GPUs, TPUs and committed use
- Alibaba Cloud GPU instances in the regions you actually run
Limitations
- Published GPU list prices change often. We use your invoice rates or a dated public source, never a remembered number.
- We do not tune model quality or replace an ML platform team.
What is not included
- Model training or prompt engineering
- Guaranteed GPU availability in a region
Author
Written by Ankit Mehta. Methods used in this engagement are documented in the related guides below.