Quick Answer
AI is expensive because the model bill is only part of the cost. Three components set the floor: model subscriptions, per-token API pricing, and infrastructure. Three more make it move: adapting models to your business, catching and fixing errors, and rising energy and datacenter costs. Efficiency doesn't fix it, because cheaper AI gets used more, not less.
Businesses are willing to spend on AI. Research from Deloitte found that in 2025, 85% of organizations increased their AI investments. This year, 91% plan to do the same.
All this spending, however, may not be delivering the efficiency companies expect. According to Forbes, the rapid adoption of AI has created a paradox, with intelligent tools now more costly than the people they replace.
With AI adoption ramping up across industries and verticals, however, businesses face a tough choice: Pull back on AI investment and risk falling behind, or go all-in and end up spending more (or much, much more) than expected.
In this piece, we’ll explore the real cost drivers of AI, examine why transparency is so important, and offer actionable suggestions to keep AI spending under control.
What are the key components of AI cost?
Three components form the basis of AI costs: Model subscriptions, token pricing, and infrastructure spending. Here’s a look at each in more detail.
Model subscriptions
AI models are typically available in two ways: Via online applications and using APIs.
For example, ChatGPT offers enterprise plans that provide access to its AI model using desktop or mobile applications. Costs are calculated per user. The more users, the higher the cost.
API-based access lets companies connect AI to in-house solutions such as CRM, ERP, HR, and other tools. This access is more expensive and is based on tokens. For example, ChatGPT’s middle API tier, GPT-5.6 Terra, costs $2.50 for 1 million input tokens and $15.00 for one million output tokens.
Token prices
Tokens are the fundamental units of AI operations. They are small chunks of data that equate to approximately 4 characters. Consider the previous sentence, which has roughly 75 characters. Doing the math (75 / 4), the sentence has approximately 19 tokens.
Given that token prices are measured by the million, it’s tempting to see this as a small cost. And it is, in isolation. Once multiple users begin running multiple projects, however, costs quickly add up.
Prices may be further inflated if AI companies raise their cost per token. For example, if server power and data processing costs rise, providers may pass these increases on to users in the form of higher token rates. According to research firm Gartner, AI coding costs will surpass the average developer’s salary by 2028, driven by rising token consumption and the shift to consumption-based licensing.
Enterprises must also be mindful of unsupervised AI projects. If teams leave AI-based tests or applications up and running, they could generate millions or billions of tokens per day, leading to very large and very unexpected monthly bills.
Infrastructure spending
Rounding out the list of AI costs is infrastructure spending.
These costs appear in two ways. If companies are using third-party AI models, the rising cost of datacenter space and performance can drive up month-to-month expenses. If organizations choose to build their own models, they are responsible for providing the power, cooling, storage, and compute infrastructure necessary, either in-house or via a cloud provider.
Report
Finance needs to prove AI’s return: CloudZero report
260 senior finance leaders (more than half CFOs) told us why the speed of seeing AI spend, not the size of it, separates who pulls ahead on AI from who gets burned.
Which AI costs change over time?
If these were the only costs of AI, companies could largely anticipate what they pay each month. The nature of AI training and outputs, however, makes this a moving target.
Evolving expenses include:
Adaptation requirements
Unless AI models are built in-house, they’re not purpose-built to match business needs. As a result, they must be adjusted and adapted to deliver usable results and ensure consistency. These adaptations require time and effort from staff, both of which are costly.
Error detection and resolution
AI improves by learning as it goes, which means mistakes are inevitable. To ensure these mistakes don’t negatively impact operations, businesses must ensure they have humans in the loop to find and address these errors, and then run further training to verify that issues have been resolved.
Hiring in-house or outsourced experts for this task is necessary, but expensive.
Environmental impacts
Research from the United Nations University (UNU) found that the expansion of AI systems has significant environmental effects. As noted by the report, “Advanced models require immense computational resources, driving high energy consumption and associated carbon emissions, water withdrawals, and land use impacts.”
Local, state, and national environmental controls may place limits on AI use or require companies to implement practices that reduce natural impacts. The expense incurred by these expectations is typically passed on to corporate consumers.
Why improved efficiency doesn’t equal lower costs
Smarter, faster, and more accurate AI makes it easier for teams to complete tasks and enhance operational efficiency.
So why are costs rising? The answer is tied to Jevons’ Paradox. In 1865, English economist and logician William Stanley Jevons argued that technological gains in efficiency led to increased resource consumption rather than less. Jevons’ example was coal: as coal mining became more efficient, both the production and consumption of this resource increased. It makes sense: As coal became easier to obtain, purchasing costs fell. This led to advancements in coal-powered technologies, which in turn led to increased demand.
AI faces a similar paradox. While AI can accomplish many tasks more efficiently, it’s often more costly than its human counterparts. To make the most of AI investments, businesses can’t leave this efficient resource idle, meaning that as efficiency improves, so does power consumption and, by extension, spending.
Consider that in 2024, U.S. datacenters used 183 terawatt-hours (TWh) of electricity. According to Pew Research, this number is on track to reach 426 TWh, a 133% increase, by 2030.
How do you control AI costs?
AI is expensive, but it doesn’t need to break the bank. Here are four ways to control evolving costs.
1. Build a business use case
Don’t spend on AI just to keep pace. This can lead to projects that sit idle or deployments that cost more to maintain than they deliver in business revenue. Instead, start with a specific use case. Identify an issue or inefficiency in current operations, determine how AI can assist, and run a pilot project to determine the impact. Equipped with a clear business case, companies are better positioned to control AI costs.
2. Prepare your data
Data determines model efficacy. Duplicate or inaccurate data can lead to skewed results, which impact business decisions. As a result, data preparation is critical. While companies can expect data preparation to consume between 25% and 35% of total project budget, skipping this step to fast-track AI deployment can lead to higher costs down the line as models struggle to produce consistent answers.
3. Always do the math
As noted above, AI costs are a combination of key components and evolving factors. To reduce the risk of AI overspend, start with a calculation such as the generative AI cost equation. Created by MIT researcher George Westerman, it states that the total cost of generative AI is as follows:
Cost of using AI models + cost of adaptation + cost of fixing errors
If the total cost of AI exceeds the cost of manual or legacy processes, projects will struggle to deliver ROI.
4. Prioritize complete operational transparency
AI is often considered a “black box”. While data inputs and outputs are clear, what happens in between is difficult to explain. As a result, transparency is key. By tracking the effort required to prepare data, the time it takes LLMs to return outputs, and the accuracy and completeness of these results, businesses can calculate total costs and identify weak links.
The price of knowledge: balancing AI and ROI
Why is AI so expensive to run? Because companies aren’t just running AI. They’re running everything associated with AI. Every test, every correction, and every adjustment to improve model outputs.
And none of these operations are free.
Balancing AI and ROI means knowing exactly how, where, and when every dollar is spent, and what that dollar does for your business. While companies can’t avoid the rising costs of AI, they can make sure that what they spend has a positive and persistent impact on day-to-day operations.
Connect every AI dollar with every business outcome. Schedule your CloudZero demo today.
Frequently Asked Questions about AI costs
