GPU Spend Is the Fastest-Growing Line Item. It's Also the Least Governed.

H100s at $32/hr. Training jobs that run until manually killed. AI teams with no cost attribution. We build the GPU cost governance layer your MLOps team hasn't had time for.

Duration: 15 business days Team: 1 Senior FinOps QA Engineer + 1 ML Infrastructure Specialist
The Challenge

You might be experiencing...

ML team running H100s with no job-level attribution — CFO sees $380K/month as a single line item.
AI experiments never get cancelled and run until someone manually kills them.
No visibility into cost-per-inference — we can't do unit economics for our AI products.

AI/GPU Cost Governance QA is finops.qa’s fastest-growing service — the 12-month first-mover window for AI cost governance is open now.

Our Approach

Engagement Phases

Days 1–4

Cost Attribution Mapping

Map GPU spend by team, project, experiment, and inference endpoint. Establish the AI/ML Cost Attribution Map.

Days 5–10

Governance Testing

Test cost attribution accuracy, idle GPU detection, training run budget controls, and inference endpoint cost tracking.

Days 11–15

Tooling & Handover

Configure Kubecost ML namespace tagging, implement idle GPU detection workflow, deliver unit economics dashboard.

What You Get

Deliverables

AI/ML Cost Attribution Map
GPU cost governance test results
Unit economics dashboard (cost-per-inference, cost-per-training-run)
Idle GPU detection and auto-shutdown workflow
Kubecost ML namespace configuration
Expected Outcomes

Before & After

MetricBeforeAfter
GPU Spend Attribution34%91%
Idle GPU Cost (nights/weekends)$28,000/mo$3,200/mo
Training Run Overruns7/quarter0
Cost-Per-Inference Tracked0% of endpoints87% of endpoints
Technology

Tools We Use

Kubecost NVIDIA DCGM Prometheus AWS SageMaker Cost Explorer
Common Questions

Frequently Asked Questions

Why is standard FinOps tooling insufficient for GPU workloads?

Standard FinOps tools were designed for instance-level attribution — one VM, one cost owner. GPU workloads are job-level: one GPU cluster may run 50 training jobs from 8 teams simultaneously. The attribution model requires job-level tagging, scheduling awareness, and GPU utilisation data that standard tools don't collect.

Do you support on-premise GPU infrastructure, or only cloud?

Primarily cloud GPU (AWS, GCP, Azure, CoreWeave, Lambda Labs). For hybrid environments with on-prem GPU clusters, we scope the engagement based on what instrumentation is available.

Get Your FinOps Defect Score

Book a free 30-minute cloud cost review. We will identify your top three FinOps gaps and give you a preliminary Defect Score — no pitch, no obligation.

Talk to an Expert