Gartner has slammed AI vendors’ lack of transparency, saying developers are facing sharply increased costs from coding agents.

Since the main AI coding agent vendors have shifted from seat-based licensing to consumption-based pricing, developer teams now face highly variable cost structures.

Developer teams face an emerging problem of escalating costs, said Nitish Tyagi, senior principal analyst at Gartner. AI coding bills were leaping from $20 or $100 to $2,000 to $5,000 per developer per month, while in extreme cases, the bill might hit $20,000 in token charges.

However, software engineering departments get little insight into how token consumption is calculated and billed, making it difficult for them to forecast and control costs accurately. AI coding vendors have not yet delivered built-in features to allow developers to optimise costs in AI coding agents, resulting in cost escalation, Gartner said.

“None of the vendors have incredible features when it comes to cost optimisation,” said Tyagi. Instead, he said vendors were focusing on the concept of “tokenmaxxing” to “boost the high” of token consumption, suggesting that if

developers increase the number of tokens, they will increase productivity gains.

“There is no direct relation between the increase in token consumption and an increase in productivity gains,” he said.

Gartner recommends developer teams optimise token consumption and adopt strategies such as context engineering practices, where software engineers improve the input context provided to AI systems. Another recommended strategy is model routing, where engineering and platform teams direct simpler, high-frequency tasks to smaller models, using frontier models only for complex, high-value work

“All of these things will improve the output quality, and, therefore, will increase the productivity gains as well, so while there is no direct relation between tokenmaxxing and productivity gains, there is a relation between optimisation of token consumption with the output quality,” Tyagi said.

As a result of the lack of cost optimisation tools among vendors and consumption-reduction strategies among users, AI development is in a situation where a developer’s coding agents may cost more than they earn, at least in some parts of the world. Gartner predicts that by 2028, AI coding costs will overtake the average developer’s salary due to rising LLM token consumption and the shift to consumption-based licensing models.

“We’re not saying AI token cost will be higher than every developer’s salary on the planet, because US salaries tend to be higher than in India, for example. But current token costs are already more than most of the salaries in India,” he said.

However, the cost of coding agents does not vary according to where they are consumed, meaning AI coding costs for a developer in India may be equivalent to the salary of an engineer with four to six years’ experience, Tyagi said.

“We’re not saying AI token costs will be higher than every developer’s salary on the planet, because US salaries tend to be higher than in India, for example,” Tyagi said, adding that token costs do not vary by location, and in India they may already be equivalent to the salary of an engineer with four to six years’ experience.