AI Cost Management: FinOps for AI, Token Pricing &; TCO

AI Cost Management: FinOps for AI, Token Pricing & TCO
Published 10/2026
Created by DCDG Partners
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Level: All Levels | Genre: eLearning | Language: English | Duration: 40 Lectures ( 2h 32m ) | Size: 1.4 GB

Build the full AI cost picture, control runaway spend, optimize without breaking quality, and run a portfolio review

What you'll learn
Understand why the model invoice is only a fraction of your real AI bill and how the other layers hide across your organization.
Map the seven layers of AI cost and draw the boundary between what you count and what you can actually influence.
Identify the costs that never make it into a business case and compare how build, buy, or subscribe reshapes the bill.
Understand how token pricing really works and why the cost curve grows faster than usage as adoption increases.
Estimate the cost of an AI workload before anyone builds it, including agents where the meter stops being predictable.
Give AI spend a real owner, build cost reporting a CFO will actually read, and catch runaway spend before the invoice lands.
Find shadow AI and the AI capabilities you are already paying for inside software your organization already owns.
Apply optimization levers including routing, gateways, caching, right sizing, commitments, batch, and rate negotiation.
Define a quality floor so cost optimization never degrades the output your business actually depends on.
Build unit economics, run a full AI portfolio review, decide what to keep or switch off, and execute your first ninety days.

Requirements
No coding or engineering required - this work happens in a spreadsheet and in conversations with people in your organization.
No prior FinOps or AI cost experience is needed - every concept is built from the ground up with a running case study.
All you need is budget responsibility over AI, or the need to answer what AI is actually costing your organization.

Description
"This course contains the use of artificial intelligence."Someone in a meeting asks a simple question: how much are we spending on AI? Somebody answers with a number, usually taken from the one invoice with the word AI printed on it. Everyone writes the figure down and the meeting moves on.

That number is usually three or four times too low. Everyone in the room is reporting the only line item that exists. The rest of the spending is real, it is ρáíd every month, and it is scattered across places nobody thinks to look.

That gap is what this course is about. It brings FinOps discipline to AI spend, and it is written for the people who have to answer that question: finance and FinOps teams, IT and procurement leaders, and anyone who owns an AI budget without being an engineer.

Most companies adopted AI quickly. Tools were rolled out, licenses were bought, and pilots became production almost by accident, all without a cost model behind them, because in the early days the amounts were too small to matter. Today they show up in the budget review.

Proving that AI generates value belongs to business cases and ROI. This course covers the other side of the equation: building the bill, finding every dollar your AI consumes, understanding why it behaves the way it does, bringing it down without damaging quality, and deciding with evidence what deserves to keep running.

You will map the seven layers of AI cost, from LLM API calls and cloud infrastructure to licenses, data and people, learn which costs never reach a business case, and see how build, buy, or subscribe reshapes everything. You will understand token pricing, why the cost curve grows faster than usage, and why agents make the meter unpredictable. You will also learn to estimate a workload before anyone builds it.

The course covers allocation and ownership, reporting a CFO will read, catching runaway spend before the invoice arrives, and finding shadow AI alongside capabilities you already own. A full module covers optimization without breaking quality: the quality floor, routing and gateways, caching, right-sizing, commitments, batch processing and negotiation.

It closes with unit economics, a complete AI portfolio review, three decisions worked through end to end, and your first ninety days.

No code and no engineering required. A single case study, runs through the entire course.

Who this course is for
Finance professionals and controllers who need to answer what AI actually costs and defend that number in budget meetings.
IT and technology leaders responsible for AI infrastructure spend who need visibility beyond the model provider invoice.
Procurement professionals negotiating AI contracts, commitments, and tiers who need to understand the cost mechanics.
Product managers and owners who need to estimate the cost of an AI workload before it gets built and deployed.
CFOs and finance directors who need AI cost reporting that is complete, credible, and actually decision-useful.
FinOps practitioners extending their discipline from cloud into AI, where the cost behaviour is fundamentally different.
Business unit leaders who have AI budget responsibility and need to justify or defend their AI spending internally.
Operations and transformation leads running AI portfolios who must decide what keeps running and what gets switched off.
Consultants and advisors helping organizations get control of AI spending and build sustainable cost practices.
Anyone who has been asked how much we are spending on AI and realized the honest answer is that nobody really knows.

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