AI coding costs may surpass developer salaries by 2028, says Gartner

AI coding costs could exceed the average developer's salary by 2028 as rising token consumption and consumption-based pricing models drive expenses higher, Gartner said. The report warned organisations to strengthen governance, monitor usage and improve cost controls to avoid budget overruns

New Delhi: AI coding costs could exceed the average developer’s salary by 2028 due to rising large language model (LLM) token consumption and the shift to consumption-based licensing models, a report said on Wednesday.

The report from Gartner Inc. said organisations moving from experimentation to scaled deployment of AI coding agents risk steep cost escalation.

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AI tokens are the units of data processed by generative AI models. Token consumption directly impacts the cost of AI coding tools, particularly under consumption-based pricing structures.

“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 licensing to consumption-based pricing among AI coding agent vendors is introducing highly variable cost structures for software engineering workloads. Many vendors lack transparency into how token consumption is calculated and billed, limiting enterprises’ ability to accurately forecast and control costs.

Without clear visibility into token usage across development tasks, organisations risk budget overruns and reduced ability to track cost-to-value outcomes.

Tyagi said that most organisations still lack the maturity and frameworks to effectively measure cost versus 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 flagged that ungoverned autonomy in agent-driven workflows, bloated context windows and the absence of structured feedback mechanisms to optimize usage may drive overspending.

The report urged leaders to establish a use-case-driven decision framework; align model selection with task complexity; mandate context engineering practices and to implement governance and cost controls