‘AI cost management has the same problems that cloud had’: Enterprises are still facing huge AI bills thanks to ‘tokenmaxxing’ – that means FinOps practices are more important than ever

The tokenmaxxing trend has swept the tech industry in 2026 and already left some firms reeling from huge bills – but lessons from the early cloud era could be key to curtailing costs.

That’s according to Patrick Brogan, director of the FinOps advisory team at DevOps firm Harness. Speaking to ITPro, Brogan said the current trend bears similarities to the cloud boom over a decade ago. Enterprises are ramping up adoption, tinkering, experimenting, and testing out what works for them.

In some cases, tokenmaxxing is simply a case of encouraging AI adoption among staff, while for others it’s about justifying lavish investment in the technology and keeping up with AI-savvy competitors.

“There’s a number of factors that go into tokenmaxxing,” he said. “Some of it has to do with organizations deliberately pushing their employees to use the technology.”

“It might come from a sort of subconscious desire, subconscious FOMO. They don’t want to feel like their competitors are leveraging technology in a way that’s going to put their own company at a disadvantage.”

Regardless of the underlying motivations, Brogan said tokenmaxxing shows companies are “throwing everything at the wall to see what sticks” then making decisions on what AI tools should be used – and it’s coming back to bite them.

The trend has reached such an extent that Harness found nearly three-quarters (72%) of organizations have been hit with unexpected cost spikes over the past year.

Uber stands out as a key example here. As ITPro reported in June, the ride hailing firm blew through its entire annual AI budget in just four months after incentivizing staff to engage in the practice.

Going back to FinOps basics

Harness noted that AI now accounts for 23% of the average enterprise cloud bill, while organizations estimate that around 26% of all AI spend is wasted. This needless waste can be tackled, though, and Brogan said time-tested FinOps practices could be key.

“AI cost management has the same problems that cloud had a decade or more ago,” he said.

FinOps is a framework for cloud spend management that aims to bring together engineering, finance, and broader business teams to maximize value and, crucially, establish accountability for spending.

It’s not a practice that’s gone away, but in the AI boom it's somewhat fallen by the wayside. Brogan said these same techniques and processes could be crucial to curtailing excessive AI spending, particularly in terms of accountability.

Harness’ study found that, in many enterprises, nobody is clearly accountable for AI costs. More than half (52%) said they had no clear sense of ownership with responsibility split across engineering, finance, and IT.

The result here is that when spending ramps up in specific areas, such as software engineering, there isn’t a single function designed to answer for it or hold things to check.

“When we were getting started in our FinOps practice there, we felt the same pain points,” he said. “You know, confusion over ownership, gaps in how we govern the cloud bill and the services that our organizations will use, and certainly invoice shock.”

Velocity creates new challenges

Brogan said the challenges posed by AI adoption in terms of cost management aren’t a facsimile of the early cloud era, however.

While that period saw a widespread shift, the scale and pace of AI integration means many organizations aren’t reacting quickly enough to surging costs.

“Those problems are compressed into a fraction of the time because AI technology is developing at such a rapid pace and its usage and adoption have exploded far faster than cloud,” he said.

“We don’t have the same 10-year span to figure out the solutions to the challenges with AI spend but, luckily, we have a lot of lessons we learned with managing cloud spend.”

Brogan noted that Harness’ findings suggest a sharpened focus on ownership and governance will be critical.

“Companies very quickly, if they haven't done so already, need to agree and align on a single owner of the AI bill,” he said.

“Whether you're a company where that's just one person, or you're a large enterprise and that requires an entire team or set of teams,” Brogan added.

“There needs to be a very clear decision on who or what that team is, the responsibilities that that team has, and what they then need to drive [in terms of] responsibility and accountability.”

Boxing clever

A key issue many enterprises fail to acknowledge lies with model choice, Brogan noted: not every task requires a costly frontier model, yet teams frequently fall into this trap.

Speaking to ITPro last month, Nitish Tyagi, senior principal analyst at Gartner, said model selection will be critical to reducing costs moving forward, particularly for smaller tasks.

Tyagi noted that “intelligent model routing” strategies are now a key focus for developer teams, helping them to box clever with selection.

Brogan echoed these comments and urged enterprises to keep closer tabs on the costs associated with individual models.

“There’s probably a lot still to be learned about how we build an application to pick the model that’s best fit for purpose,” he said.

“[So] not using a frontier model for something that can be delivered by an older generation. Maybe it arrives 10% slower at that outcome, but if you can live with that trade-off for the lower cost, then that should be built into the application design from the start.”

Whether enterprises are willing to accept this trade-off is debatable, Brogan noted, and Harness’ study suggests there’s little sign that they’ve learned lessons so far.

More than half (57%) of engineers told the firm they’re still being encouraged to engage in tokenmaxxing practices, for example.

Some organizations have taken drastic measures to cut down on needless spend. As ITPro reported in late June, Accenture told staff to cut down on AI use for basic tasks due to “"soaring token spend"..

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