The jump is already visible. AI coding bills are leaping from $20 or $100 a month per developer to $2,000 or even $5,000, Tyagi told The Register. The driver is a quiet change in how the tools are sold.

Vendors used to charge a flat seat fee. Now most charge by consumption. A developer pays for what an agent uses, and an agent can use a lot. That shift turns a predictable line item into a wild one. It is the same dynamic that has already seen enterprise AI bills triple even as token prices fell.

Consumption pricing rewards the vendor when usage grows. The more an agent writes, tests and retries, the more it bills. A single autonomous task can chew through tokens a developer never sees. Multiply that across a whole team, and the monthly invoice swells.

None of that means the tools fail. Used well, they ship features faster and free engineers from grunt work. The worry is the gap between the promise and the invoice. Right now, far too few teams measure it, and fewer still act on what they find.

Nobody can see the bill

The deeper problem is visibility. Many vendors do not show how they calculate or bill token use, Gartner said. Companies cannot forecast the cost, so budgets run dry early. “Most organizations still lack the maturity and frameworks to effectively measure cost versus business impact,” Tyagi said.

Gartner is blunt about the result. Engineering leaders find token-driven spend harder and harder to justify. Budgets vanish sooner than planned. And without a way to tie spend to business value, the next budget meeting gets awkward.

Developers are not the ones policing it. They optimise for speed, not cost. “Token discipline will not emerge through developer choice alone,” Tyagi said. Without rules, he warned, costs can climb faster than the productivity the tools promise.

Why the costs keep climbing

Several forces push the meter up. Agents left to run on their own burn tokens freely. Bloated context windows, where the tool receives more text than it needs, add more. And teams rarely build a feedback loop to trim the waste.

The tools themselves offer little help. AI coding vendors have not built in mature cost controls, Gartner said. So the job of restraint falls on the buyer. Most buyers are not ready for it.

The user base is changing too. As people grow comfortable with the tools, light users become heavy users. Gartner expects model prices to rise as well, as AI firms chase profit. More usage, at a higher price, points one way.

This is already reshaping behaviour. Some firms have started to cap how much AI their staff can use. The most AI-hungry companies now spend around $7,500 per employee each month.

The industry scrambles for a fix

The pain has created a market. Database vendors are now pitching themselves as the cure for runaway AI costs, arguing they can cut the number of calls a coding agent makes. Others want an industry standards body to explain the bills.

Even big players are pulling back. Microsoft quietly retreated from heavy Claude Code use over cost, and GitHub paused some Copilot sign-ups as agentic demand strained the economics. The tools work. Paying for them at scale is the hard part.

Gartner sees the wider market entering a fresh phase of expansion and competition. That should, in time, bring better cost tools and clearer pricing. For now, the buyers sit ahead of the products. They are scaling fast on tools that were never built to be cheap.

What Gartner says to do

Gartner's advice is about discipline, not retreat. It tells engineering leaders to sort tasks into three buckets: developer-led, developer-with-agent, and fully agent-led. Each one gets a set level of autonomy.

Take model routing first. A frontier model is overkill for a simple function. Gartner wants teams to send easy, high-frequency tasks to smaller, cheaper models, and reserve the expensive ones for complex work. Done well, that alone can trim the bill sharply.

Context engineering is the other lever. Every extra line fed to the model costs tokens. Trim the input to what matters, summarise the rest, and the meter slows. Then set token limits, monitor usage automatically, and review the heaviest workflows in every sprint, rather than panicking once the budget runs out.

The bottom line

None of this kills the case for AI coding. The tools genuinely speed work up, and few teams will hand them back. But Gartner's forecast is a warning that the savings are not automatic. A tool that writes code faster is no bargain if it costs more than the person using it.

The open question is whether discipline arrives before the bill does. Pricing keeps rising, usage keeps growing, and the visibility stays poor. 2028 is not far away. The companies that win will be the ones that learn to count tokens before the tokens count them.