AI coding cost will surpass average developers’ salary by 2028: Gartner
Enterprises are facing the challenge of rising costs from LLM usage and there is easy fix to this problem.
The artificial intelligence (AI) coding costs will overtake the average developers’ salary by 2028 due to rising large language model (LLM) token consumption and the shift to consumption-based licensing models, according to Gartner.
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 optimize 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,” Tyagi said.
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.
“Most organisations still lack the maturity and frameworks to effectively measure cost versus business impact,” said Tyagi. “Software engineering leaders are increasingly concerned as token-driven AI spend becomes harder to justify, with budgets often being depleted earlier than expected.”
Beyond pricing and visibility challenges, how AI coding agents are used within organisations is further driving cost pressures. 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 optimize usage.
In addition, AI coding vendors are yet to deliver mature, built-in cost optimisation capabilities in AI coding agents, further contributing to cost escalation.