AI spend is accelerating - we help you keep up.
AI is not a product or feature that stands alone. Rather, it is a new model that sits across SaaS, cloud, data platforms, and infrastructure.
Most organisations see AI adoption and spend accelerating, but few have the insights and governance to control it.
Our FinOps for AI services help you establish visibility, ownership, forecasting discipline, and commercial control before AI spend becomes normalised and embedded.
Why FinOps for AI?
AI changes how technology is consumed:
Costs are consumption & usage-led
Tokens, prompts, GPUs, and API calls replace predictable licence metrics
Embedded AI features drive SaaS license uplifts
Pilot projects quickly become permanent run-rate costs
AI software and services are introduced with no exit plan
Value realisation lags behind cost acceleration
Without modern governance, AI creates cost and risk. With it, AI creates business value.
We tailor our approach to where you are:
A structured assessment to identify, categorise, and quantify all AI-related spend across SaaS, cloud, data platforms, and embedded licensing. Establishes a clear baseline and exposes hidden cost drivers.
Full visibility of direct and indirect AI costs
Identification of embedded AI licence uplifts and add-ons
Mapping of token, inference, and GPU consumption
Detection of fragmented or duplicated AI initiatives
Executive-ready AI cost baseline for reporting and planning
Design and implementation of allocation models that link AI consumption to teams, products, or use cases. Introduces unit economics appropriate for AI workloads.
Clear ownership of AI consumption
Cost per prompt, per user, per model, or per business transaction metrics
Reduced centralised “shadow” AI spend
Improved accountability across engineering and business teams
Foundation for chargeback or showback models
Commercial and entitlement review of AI add-ons, copilots, premium tiers, and credit-based models across Microsoft, Salesforce, ServiceNow and other SaaS providers.
Reduction of unused or low-value AI add-ons
Clarity on pooled vs per-user AI credit models
Improved negotiation position at renewal
Avoidance of modern “AI shelfware”
Clear entitlement tracking for AI features
Design of governance controls that integrate ITAM, FinOps, procurement, and data governance into a coherent AI operating model.
Defined ownership for AI spend
Clear guardrails for experimentation vs production
Policy framework for training rights and data usage
Reduced contractual and compliance risk
Sustainable scaling of AI initiatives
Book a consultation today!
Speak with our team to find the best solutions for your needs!