FinOps for AI

AI spend is now a line item no organisation can afford to ignore. As Copilot, AI agents and large language model (LLM) tools deploy at scale, the invoices arrive before the governance does.

Organisations deploying AI tools are accumulating costs with no visibility into consumption, AI subscription efficiency, or business return.

Token spend is routinely absorbed by experimentation with no defined outcome, no link to revenue, and no framework a CFO can translate into value. The result is unbudgeted expenditure that is difficult to justify and harder to govern.

Our experts in Software Asset Management apply proven discipline and methodologies to AI cost management, bringing the same rigour that controls your software estate to your AI estate.

FinOps for AI – optimising AI spend

Our FinOps for AI service helps businesses optimise cloud spending by delivering timely analytics and automated cost-saving recommendations, all powered by advanced machine learning.

Unlike traditional solutions, our service offers predictive insights, seamless integration with leading cloud providers, and continuous monitoring to ensure your organisation maintains optimal efficiency and cost control.

Visibility and baselining

A fixed-scope assessment that maps every AI tool in use, identifies which teams and agents are driving consumption, and establishes your current AI subscription tiers and cost baseline. Critically, spend is mapped against business units and use cases, creating the foundation for a value and ROI conversation rather than a cost-only view.

ROI alignment and business value mapping

Bring control to AI spend with a structured governance framework. We help you manage budgets, monitor consumption, and improve vendor oversight, so every investment is aligned to business value, not open-ended experimentation, giving finance and technology leaders clearer accountability.

AI subscription and tier optimisation

An analysis of whether current AI subscription decisions are commercially sound. This covers right-tiering recommendations, bulk token purchasing strategy, API gateway evaluation, and broker options, producing a prioritised optimisation plan with quantified savings.

Token optimisation

Reduce AI costs without compromising performance. Our service applies FinOps principles to token usage, helping you improve model choice, prompt efficiency, caching and processing. The result is clear, prioritised actions that lower AI spend and improve cost efficiency.

Governance framework

A structured approach to AI tool procurement and consumption management, covering budget allocation by business unit, consumption monitoring, and vendor management. Governance ensures that spend is approved against defined value criteria, not absorbed by open-ended experimentation.

Ongoing managed service

Continuous monthly monitoring of AI consumption as a natural extension of your existing SAM managed service, providing regular reporting against agreed cost and value targets.

FinOps for AI benefits

By adopting FinOps for AI, organisations gain a range of advantages that streamline operations, enhance visibility, and drive cost savings.

CFO-readable view of AI spend

AI costs are translated from token volumes and AI subscription tiers into business value terms. Finance leadership gains a clear basis for evaluating return on AI investment, challenging spend that lacks a defined outcome, and making future budget decisions with confidence.

Cost reduction, quantified

Right-tiering and purchasing optimisation produce measurable reductions in AI tool spend. Where gain-share is the agreed commercial model, Version 1’s fee is tied directly to identified savings.

Spend directed at outcomes, not experimentation

Mapping consumption against business activity identifies where AI spend is generating value and where it is funding undirected experimentation. That distinction enables informed decisions about which use cases warrant continued investment.

First visibility into consumption

Most organisations have no clear picture of which teams, tools or agents are driving AI spend. The baseline assessment delivers that visibility for the first time.

Reduced budget and compliance risk

Unmanaged AI spend creates exposure across budget accountability, data governance, and vendor contractual terms. A structured service reduces all three.

The problem does not recur

The ongoing managed service component ensures cost control and value tracking are continuous, with anomalies identified at monitoring point rather than invoice point.

Frequently asked questions

What is FinOps for AI?

FinOps for AI applies the same financial operations discipline used to manage cloud spend to AI tool consumption. It covers visibility into what is being spent, by whom, on which tools, and whether that spend is generating proportionate business value. The practice is emerging directly from cloud FinOps as organisations recognise that AI costs carry the same governance risks that unmanaged cloud spend did between 2015 and 2020.

What is token optimisation?

Most AI tools charge based on token consumption, the unit of input and output processed by the model. Token optimisation identifies where consumption is inefficient, whether through poor prompt design, unnecessary model complexity for a given task, redundant processing, or lack of caching. Reducing token waste lowers the unit cost of AI delivery without reducing capability or output quality.

What is the difference between FinOps for AI and token optimisation?

FinOps for AI is the broader discipline covering commercial decisions, governance, AI subscription, and value alignment. Token optimisation is a specific technical lever within that discipline, focused on reducing consumption inefficiency at the point of use. Both are needed: purchasing at the right tier means little if consumption remains wasteful, and reducing token waste means little if the AI subscription model is misaligned.

Why do I need FinOps for AI?

AI spend is growing faster than the governance frameworks to manage it. Most organisations have no consolidated view of what they are spending, which business units are consuming most, or whether that spend maps to any defined return. Without a management discipline, AI costs behave the same way early cloud costs did: they accumulate, they surprise, and they are difficult to justify to finance leadership when scrutiny arrives.

We are still in early-stage AI adoption. Is this relevant to us?

Early-stage adoption is precisely when governance frameworks are easiest to establish. Retrofitting cost management discipline onto a mature, distributed AI estate is significantly harder than building it from the outset. Organisations that govern AI spend from the start avoid the bill shock and remediation costs that early cloud adopters experienced.

How is this different from what our AI vendors already provide?

Vendor portals provide consumption data for their own tools in isolation. They do not consolidate spend across multiple AI tools, map consumption to business outcomes, assess whether AI subscription tiers are commercially optimal, or provide independent recommendations. We operate vendor-agnostically, with the commercial and AI subscription expertise to challenge vendor pricing and terms.

Who owns this problem in our organisation?

In practice, it sits between IT and Finance. The IT Director or CTO typically controls tooling and vendor relationships; the CFO carries the budget exposure when AI costs exceed forecast. Our service is designed to give both functions a common view of spend, value, and risk, removing the gap that currently exists between the two.

Version 1 manages AI tool consumption internally across its own organisation. The findings informed how this service was designed. We can do the same for yours.
Version 1 SAM team