The Total Cost Of Ai Ownership

The Total Cost of AI Ownership (AI TCO) | Cohere

Jul 15, 2026

10 minute read

The total cost of AI ownership

For many enterprises across industries, the use of artificial intelligence has moved from a curious experiment to a necessary expense, with their digital transformation strategies growing alongside AI innovation. Yet, many companies still do not fully understand the unit economics of what they are getting when purchasing AI technologies, which can turn into a financial risk as AI workflows scale in ambition and become a permanent part of conducting business.

At Cohere, we recognize our customers’ challenges related to the total cost of AI ownership (AI TCO) and the strategic decisions over when to own AI technology versus when to rent it — and AI ownership is not one size fits all. In this post, we’ll walk through both the invoiced and hidden costs of running AI in your enterprise and how to approach your AI ownership strategy.

To connect with Cohere sales to see your total cost of AI ownership, and discuss opportunities to lower it, please contact us.

How AI costs are calculated

The visible price of AI is usually quoted in tokens. Tokens are the fragments of text that a model both reads and generates. Tokens are incurred across every prompt, response, retrieved document, agent step, tool call, loop, and retry. Yet token pricing is only one part of cost — albeit the most visible and unpredictable given the way that AI usage scales when effectively implemented within an enterprise.

The larger question, however, becomes not what a vendor charges per million tokens, but what it costs to own, operate, secure, and scale AI once it becomes an indivisible aspect of how business is conducted. This is the core challenge that AI ownership presents to organizations.

AI TCO is rising alongside usage

Today, the total cost of ownership is increasing as enterprise AI becomes a major business expense. Gartner projects global AI spending will reach $2.52 trillion USD in 2026, a 44% annual increase, driven primarily by infrastructure. It matters more as AI adoption increases, because AI maturity and usage results in more inference, longer context windows, more agentic steps, increased retrieval, and demand for speed.

One answer on a screen may represent a chain of model calls, document searches, tool invocations, and validation steps — and these steps are not hidden in infrastructure or model invoices.

Layers of AI ownership: Renting and owning infrastructure

As AI becomes a standard business cost, firms must understand what they are purchasing: tokens, latency, throughput, utilization, data control, and the ability to forecast expenses. Enterprises, specifically financial leaders and those close to AI transformation initiatives, must focus on which variables drive costs and which ensure control.

Firms that scale AI profitably will not be those that spend the most, but those that work out which AI capabilities to own and which to rent — and build the discipline to tell them apart. Those that rent everything reach the same conclusion eventually, when the cost grows to become unsustainable or the control quietly slips away.

The hidden costs of AI

Token economics is the obvious starting point because it explains why AI spend behaves differently from that of traditional software. In conventional cloud computing, costs map to familiar units: servers, storage, bandwidth, and time. AI introduces a more volatile unit of account.

A token is small, but the systems built on top of tokens are not. A single user request can trigger a long prompt, large context window, multiple reasoning steps, several tool calls, and a polished final answer. The user experiences one interaction; the company pays for the entire chain.

This is why AI costs can surge without visible product changes. Teams may expand context windows for better answers, add retrieval to reduce hallucinations, introduce agent loops for complex tasks, or route requests to larger models — all sensible decisions that, in combination, dramatically alter the cost profiles.

Looking at the AI system at large

The common mistake is treating token price as unit economics. It's not. Total cost of ownership is the system that produces the token. The relevant questions are broader:

1. How many tokens does a task consume? 2. How many model calls sit behind a user action? 3. Which model handles each step and how is it routed? 4. How much context is passed? 5. How often does the system retry? 6. What latency does the business require? 7. Is the paid-for infrastructure actually being utilized?

A great deal of this is driven by infrastructure, but also by the imperative that companies must be on the cutting edge of digital innovation in order to stay competitive. As such, companies tend towards the consumption of the largest models that are at the forefront of commercially available innovation. For example, an earnings call from Uber stated that “10% of the company’s committed code is built by autonomous agents,” which coincided with the company spending 12 months of its AI budget in only four.

Headlines abounded, but the question remained: AI created more productive organizations, but for leaders concerned with revenue forecasts, innovation cannot come at the price of inaccurate forecasting.

The surprise is already visible across markets

Recent research highlights the scale of unexpected AI infrastructure costs. According to a December 2025 IDC InfoBrief commissioned by DataRobot, 96% of organizations deploying generative AI and 92% using agentic AI faced higher-than-expected costs. These widespread overruns indicate that current planning models do not align with actual workloads. Gartner anticipates that AI will enter the "trough of disillusionment," where the main barrier to scaling is the unpredictability of returns, not lack of ambition.

A November 2025 McKinsey survey of nearly 2,000 organizations in 105 countries found that while AI adoption is widespread, only about one-third are scaling it enterprise-wide, and just 5-6% report significant financial impact. The lesson is clear: unpredictable spending, without clear value, penalizes poor cost management.

Forecasting and governance remain challenging. A 2025 survey by Mavvrik and Benchmarkit found that 80% of companies miss their AI infrastructure forecasts by over 25%, and nearly 25% miss by more than 50%. Only 15% are within 10% of their...