New Delhi — The cost of using artificial intelligence coding tools could exceed the average developer’s salary by 2028 as large language model token consumption rises and vendors adopt consumption-based pricing, according to a report released Wednesday.
Gartner said companies moving from experimenting with AI coding agents to deploying them at scale face the risk of rapidly increasing costs.
AI tokens are units of data processed by generative AI models. The number of tokens used directly affects the cost of AI coding tools under consumption-based pricing models.
“Organisations are rapidly moving from experimentation to scaled deployment of AI coding agents, but many are underestimating the financial impact of rising token consumption,” said Nitish Tyagi, Sr. Principal Analyst at Gartner.
“Token discipline will not emerge through developer choice alone, as developers tend to optimise for speed and convenience over cost efficiency. Without a governed engineering operating model, costs can escalate faster than the productivity gains these tools are designed to deliver,” he added.
The shift from seat-based licenses to consumption-based pricing is creating less predictable costs for software engineering workloads. Gartner said many vendors do not provide enough transparency about how token use is calculated and billed, making it difficult for companies to forecast and control spending.
Organizations without clear visibility into token use across development tasks risk exceeding their budgets and may struggle to measure whether the tools deliver sufficient value.
Tyagi said most organizations still lack the frameworks needed to compare AI coding costs with their business impact.
“Software engineering leaders are increasingly concerned as token-driven AI spend becomes harder to justify, with budgets often being depleted earlier than expected,” he added.
The report said excessive autonomy in agent-driven workflows, unnecessarily large context windows and the lack of structured feedback systems could contribute to overspending.
Gartner urged software engineering leaders to establish use-case-based decision frameworks, select models according to task complexity, require context-engineering practices and implement stronger governance and cost controls. (Source: IANS)
