AI spend governance
AI Spend Governance Review
AI cost can grow differently from traditional cloud infrastructure. LLM calls, tokens, GPUs, experiments, agents, pipelines and inference workloads need early visibility and ownership.
AI cost questions to answer early
- Which product, team or customer owns AI usage?
- Are experiments separated from production?
- Is spend tracked by model, API, endpoint or feature?
- Are budgets and alerts in place?
- Can finance forecast usage growth?
- Are runaway loops, agents or inference spikes detectable?
- Is the business value of usage measurable?
Governance areas reviewed
LLM/API usage
Cost per API call, token patterns, usage limits, ownership.
GPU/compute
Utilization, scheduling, idle capacity, training/inference separation.
Product allocation
Feature, customer, product or business-unit mapping.
Experiment control
Sandbox budgets, approval thresholds, expiry dates.
Forecasting
Pilot-to-production cost assumptions.
Anomaly response
Alerts, owners, escalation and rollback paths.
Experiment to scale governance
Each phase should have a budget, owner, metric and review cadence before production usage becomes difficult to explain.
- Experiment: budget, owner, metric, review cadence
- Pilot: budget, owner, metric, review cadence
- Production: budget, owner, metric, review cadence
- Scale: budget, owner, metric, review cadence
Deliverables
- AI spend risk summary
- Usage ownership model
- Budget and anomaly control recommendations
- Cost metric recommendations
- Experiment-to-production governance model
- 30/60/90-day action plan
FAQ
Common questions
What is FinOps?
FinOps is an operating model that helps engineering, finance, product and leadership teams collaborate on technology spend, value and accountability.
Do you only focus on cost cutting?
No. The focus is visibility, allocation, governance, forecasting and better engineering decisions. Optimization is part of the work, but it comes after the team understands ownership and operating cadence.
Which cloud platforms do you support?
AWS, Azure, hybrid environments and AI workloads. The first assessment clarifies the exact scope and data available.
Do you support AI cost governance?
Yes. AI spend governance covers LLM/API usage, GPU or compute patterns, experiment controls, production usage, budget alerts, anomaly response and ownership.
Do you work with MSPs or cloud consultancies?
Yes. Partner and white-label FinOps support is available for MSPs and cloud consulting firms.
Is pricing public?
Pricing depends on scope, environment size, stakeholder needs and deliverables. The first step is a discovery call.