White paper
Cost-Intelligent AI Adoption
AI spend grows with usage, not with licences. This paper shows how to place each workload on the right tier of AI, and how to see and control the cost across all of them.
Why it matters
- Forecast worldwide AI spending in 2026, according to Gartner
- US$2.59tn
- Expected growth in AI spending over 2025
- 47%
- Respondents to the State of FinOps 2026 survey now working to manage AI spend, up from 63% a year earlier
- Nearly all
Sources: Gartner, 19 May 2026; FinOps Foundation, State of FinOps 2026 (1,192 respondents).
What you will learn
The tri-modal strategy
Three tiers defined by how they are priced: SaaS AI per seat, private models on fixed capacity, premium models per token. When each one fits, and a break-even test for private capacity.
The AI FinOps layer
One place to see, allocate, route, limit and protect. Practical cost levers: caching, batching, model routing, context limits and agent budgets.
A decision order that holds
Data sensitivity first, then complexity, then volume, then a test. Includes a decision tree and a table of workload categories.
Inside the paper
- An illustrative cost allocation, before and after optimisation
- Considerations for the UAE, Saudi Arabia and Bahrain: data residency and national data-protection laws
- A 30, 60 and 90-day roadmap, with risks and mitigations
- A one-page checklist to start the following week
17 pages, A4. Written by Mohan Krishnamurthy, General Manager, Evanssion. For CIOs, CTOs, CFOs, CISOs and heads of AI and FinOps.
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