Measure
What does AI fully cost, where is it consumed, and who owns it?
Most of the cost of AI sits below the model invoice: platform, engineering, review and verification, governance, and change. Measure establishes the fully loaded cost, where it is consumed and who answers for it, so that any later claim of value is set against an honest denominator.
Do this
Templates, guides and methods for this phase, in a sensible order.
Excel worksheet, v1.0
AI TCO worksheet
Price an AI use case from pilot to production across seven sheets, including the costs that never reach the model invoice.
Browser tool, v0.5
Fully loaded inference cost estimator
Build a monthly inference estimate from the rate card up, adding output ratio, caching, uplifts and fees. Runs in your browser; nothing is sent anywhere.
Excel template, v0.5
AI Portfolio and Cost Register
One register of initiatives, tools and agents with owners, and one cost register in the seven layers of the AI TCO Framework.
Guide
The First 100 Days of AI Value Management
How to start AI value management: a week-by-week first month, then a plan to a first portfolio review, board pack and operating model in 100 days.
Guide
What You Actually Need to Measure AI Value: The Instrumentation Blueprint
The cost, usage, quality, outcome and portfolio data AI value measurement needs, where each comes from, and the join that often breaks: workload identity.
Guide
The Economics of Model Selection: Use the Cheapest Setup That Clears the Quality Floor
Why 'which model is best?' is the wrong question: compare setups on cost per successful outcome, and see where routing, caching and human checks fit.
Reference
Programmatic Access to AI Costs: A FinOps Practitioner's Billing API Guide
How FinOps and platform teams can pull AI cost data from model providers, hyperscalers and coding tools, what is missing, and a minimal aggregation pipeline.
Related pages
Practical pages that sit alongside the templates.
The thinking behind it
The models and evidence that explain why this phase works as it does, in the Understand section.
Reference explainer
AI TCO Framework
Seven cost layers, most of which never appear on the model invoice.
Reference explainer
Token economics explained
The anatomy of a token bill: input and output, caching, context, agent loops, and where the meter ends.
Adapted concept
The AI iceberg
Value is the visible tip. The submerged base is what keeps it afloat, and what most programmes forget to fund.
Essay
What Cloud Taught Us About the Real Cost of AI Inference
Why per-token rates understate enterprise inference bills, what rate cards miss, and how finance and FinOps leaders can estimate fully loaded inference cost.
Reference
What AI Actually Costs: Reference Cost Ranges for Enterprise AI
Reference cost ranges for each layer of enterprise AI spend, from inference and platform fees to governance and integration. A starting point for planning.
Essay
Embedded AI, Hidden Tokens: Why SaaS Pricing Obscures AI Economics
How AI bundled into SaaS subscriptions hides token consumption and creates procurement blind spots. A guide for CFOs and procurement leaders.
Next phase
Each phase feeds the one after it.