By 2028, AI coding costs will overtake the average developer’s salary due to rising large language model (LLM) token consumption and the shift to consumption-based licensing models, forecast Gartner, a Stanford-based business and technology insights company.

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

Nitish Tyagi, Sr. Principal Analyst at Gartner said, “Organisations are rapidly moving from experimentation to scaled deployment of AI coding agents, but many are underestimating the financial impact of rising token consumption.’’

Token discipline would not emerge through developer choice alone, a developers tend to optimise for speed and convenience over cost efficiency, Mr. Tyagi said adding, “Without a governed engineering operating model, costs can escalate faster than the productivity gains these tools are designed to deliver.”

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

“Most organisations still lack the maturity and frameworks to effectively measure cost versus business impact,” said Tyagi further said, “Software engineering leaders are increasingly concerned as token-driven AI spend becomes harder to justify, with budgets often being depleted earlier than expected.”

Usage patterns, governance also driving costs

Beyond pricing and visibility challenges, how AI coding agents are used within organisations is further driving cost pressures, reported Gartner. Token overspending is often linked to how software engineering leaders govern usage, with common failure modes including ungoverned autonomy in agent-driven workflows, bloated context windows and the absence of structured feedback mechanisms to optimise usage, the firm added.

“AI coding costs will continue to rise as infrastructure investment and profitability challenges push model pricing higher,” said Tyagi. “At the same time, as more developers adopt AI tools, light users are expected to rapidly become mainstream users as familiarity and reliance increase, driving further growth in token consumption and overall spend.”

On establish a use-case-driven decision framework, Gartner suggested that organisations should clearly define when AI coding agents should be used and determine appropriate levels of autonomy for each task. This includes classifying development tasks into three execution models: developer‑led, developer‑with‑agent, and fully agent‑led, as per the analyst firm.

As per Gartner analysis, AI coding agents are most cost-effective when work is broken into smaller tasks that can be handled by smaller models, with escalation only when complexity demands it. Engineering and platform teams should implement intelligent model routing strategies that direct simpler, high-frequency tasks to smaller models while reserving frontier models for complex and high-value development work.