Controlling the Cost Per Task Completed - Beyond Cost Per Token
The assessment
In two years, the share of FinOps teams managing AI spending has risen from 31% to 98%. The bills arrived before governance was ready. The actual cost of a GenAI feature in production is typically two to four times the cost of the foundation model tracked by the finance department; encompassing infrastructure, orchestration, vector storage, and human review.
Agentic AI exacerbates the problem through agent loops, cascading tool calls, and multi-model orchestration. Uncontrolled consumption can burn through a quarterly budget in a single weekend. Traditional cloud FinOps levers, such as reserved instances and month-end alerts, are no longer sufficient.
Our approach
Gabriel Greenfield applies his Meridian methodology to structure the economic management of AI in three stages: Suggest, Facilitate, and Support. This approach avoids the pitfall of FinOps measures bolted on as an afterthought: financial governance is designed as an integral part of the AI architecture, rather than as an ex-post control mechanism.
Phase | Objectif | Ce que nous livrons |
|---|---|---|
Suggérer | Diagnostiquer l'exposition réelle aux coûts IA | Cartographie des dépenses par cas d'usage, scoring GAME, quick wins priorisés |
Faciliter | Mettre en place le cadre du pilotage | Métriques coûts/tâches, budgets-enveloppes, garde-fous agentiques, comités FinOps AI |
Accompagner | Ancrer la discipline dans la durée | Revue trimestrielles de rightsizing, formation des équipes, preuve d'audit ISO 42001 |
The key metric: cost per completed task
The cost per token or per inference says nothing about the value produced. An agent who fails three times before succeeding costs more than it appears on the gross bill. We build management around the cost per successfully completed task — the only unit that links spending and business results, and which allows us to arbitrate between models, architectures and levels of automation on a common basis.
An extension of the GAME framework
The stake is based on GAME (Gabriel Greenfield AI Maturity Evaluation) - our proprietary framework aligned with ISO 42001 and the NIST AI RMF, enhanced with a dedicated "Economy & Value" dimension. It assesses each organization's ability to allocate, forecast, and manage AI spending, ranging from the "incurred expense" stage to the "cost managed as a decision-making asset" stage.
A bridge to compliance
Managing AI costs also entails documenting its usage. FinOps deliverables, such as attribution by use case, budget envelopes, and the history of trade-off decisions, provide evidence of governance that can be directly leveraged for AI Act audits (covering transparency obligations and high-risk categories) and ISO 42001 compliance.
Deliverable
Why Gabriel Greenfield
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Mapping of AI spending by use case rather than by vendor invoice
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GAME maturity score, including the Economics & Value dimension
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Dashboard showing cost per completed task, by use case
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Budget-envelope policy with alert thresholds and kill switches for agents
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Model rightsizing roadmap (frontier → specialized)
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Governance evidence kit for ISO 42001 / AI Act audits
Most AI FinOps players come from the world of cloud tooling and are learning about agentic systems as they go. Gabriel Greenfield combines three rarely united skill sets: responsible AI governance (ISO 42001, AI Act), agentic system architecture, and token economics. This offering is not just another dashboard—it represents the convergence of these three disciplines within a single operational framework.
Contact
François Rivard — Founder, CEO & Chief
francois.rivard@gabrielgreenfield.com
+85292205826 (Whatsapp)
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