The cost of using artificial intelligence (AI) coding tools could surpass the average developer’s salary by 2028 as companies increase adoption and AI usage grows, according to Gartner, Inc., a business and technology insights company.
The increase is being driven by large language model (LLM) token consumption. Tokens are the units of data that AI models process, and higher usage can result in higher costs, especially as more AI coding tools shift to consumption-based pricing instead of fixed subscription fees.
“Organizations 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, senior 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.”
Gartner said many organizations are struggling to predict AI coding expenses because vendors increasingly charge based on usage. Unlike traditional software licenses with fixed monthly or annual fees, consumption-based pricing can change depending on how frequently AI tools are used and how much data they process.
The lack of visibility into token usage makes it harder for businesses to track spending, manage budgets, and measure whether AI tools are delivering enough value.
“Most organizations 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.”
Gartner noted that cost increases are also linked to how companies manage AI coding tools. Uncontrolled AI agent usage, large amounts of unnecessary information being sent to AI models, and limited monitoring can quickly increase expenses.
“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.”
To control costs, Gartner recommends that software engineering teams set clear rules on when AI coding tools should be used, match simpler tasks with smaller AI models, train developers to provide only relevant information to AI systems, and monitor high-usage activities.
The research comes as companies worldwide, including businesses in the Philippines, continue exploring AI tools to improve software development speed and productivity. However, Gartner’s forecast highlights the need for organizations to balance AI adoption with cost management strategies.