AI Economics glossary
65 terms on what AI costs and how to manage its value.
Frequently asked questions
What is the AI Economics glossary?
It is a reference of more than 60 terms used to understand and manage the cost of artificial intelligence, from tokens, context windows and inference pricing to GPUs, model routing, AI budgets and AI ROI. Each entry gives a short definition first, then explains what the term means in practice for IT financial management.
Who is the AI Economics glossary for?
IT finance and FinOps teams, technology and AI leaders, engineers and architects who build with AI models, and the finance and business leaders who fund AI initiatives and need to judge their returns.
How are the terms organized?
Terms are listed A to Z and tagged by category: AI economics fundamentals, tokens and pricing, infrastructure and compute, cost optimization, and value and governance. Use the search box or the category filters to narrow the list, or jump straight to a letter.
How does AI economics relate to FinOps and TBM?
FinOps manages cloud spending and TBM manages technology spending as a whole. AI economics applies the same disciplines, visibility, allocation, optimization and value tracking, to AI costs such as model usage, GPUs and AI software, which behave differently from most other technology costs. See the FinOps & Cloud glossary.
How does AI economics fit into the Enterprise Operating Model?
AI economics sits in the Fund & Optimize part of the Enterprise Operating Model. Connecting AI costs to the capabilities, products and outcomes they support lets leaders fund the AI work that delivers value and adjust or stop the work that does not. Explore AI token economics.