AI coding token costs are on track to rival human payroll

Enterprises may soon be paying as much for their developers’ AI token usage as they do for their salaries.

, it reflects a trend toward consumption-based licensing models as vendors balance infrastructure investments with profitability. Rather than the flat per-seat explained that it’s important to note that Gartner’s prediction is based on a global average salary of $2,000 per month; it doesn’t mean AI token usage will exceed all salaries. For instance, in the US, yearly pay rates can be six digits or more.

However, that kind of spend is not out of the realm of possibility, Tyagi emphasized. “I have heard scary numbers like ‘My developer consumed $20K last month,’ or ‘A business user consumed $32K’.”

If these amounts sound shocking, that’s the point. “The goal is to alarm the industry about the impact of token cost if it is not governed and controlled,” he said.

Lack of visibility, immature oversight

Enterprises are quickly moving from experimentation to scaled deployment of is not directly related to higher productivity gains,” Tyagi said, “but optimizing token consumption is.”

Still, this in no way means that organizations should move away from AI coding agents, he emphasized. Optimizing token consumption simply means spending only as much as needed without compromising the quality and value brought by AI.

“Without a governed engineering operating model, costs can escalate faster than the productivity gains these tools are designed to deliver,” Tyagi said.

How enterprises can control token usage

The traditional ‘lines-of-code-written’ productivity metric no longer applies when AI can almost instantaneously produce entire Python libraries. Rather, value should be measured in quality, speed, and customer satisfaction metrics, Tyagi said.

For instance: How quickly are developers able to release important features? How much time is reduced between app development and feedback from business, product, and development teams? Shipping features quickly while maintaining quality can create competitive advantage and improve user and customer experience, he said.

Gartner also advises establishing strong governance and cost controls. For instance, introduce token thresholds, automate usage monitoring, and create explicit escalation policies.

“Embedding these controls into engineering workflows ensures consistency and prevents uncontrolled cost growth,” the firm notes.

In addition, enterprises should create a “use case driven” decision framework. This means clearly defining when AI coding agents should be used, and their appropriate levels of autonomy given certain tasks. Further, classify those tasks into three execution models: ‘developer‑led,’ ‘developer‑with‑agent’, and ‘fully agent‑led.’

Enterprises should also select models based on task complexity. Break work into smaller tasks that can be performed by smaller models, “with escalation only when complexity demands it,” Gartner advises. Engineering teams should route workflows deliberately, directing simpler, high-frequency tasks to smaller models and using frontier models only for complex and high-value work.

Another cost saving tactic is mandating specific context engineering practices, the firm says. Developers should be trained to optimize the context they input to AI, including only the information that’s relevant, summarizing that content as much as possible, and eliminating unnecessary data.

Further, teams should embed token usage reviews into development cycles. Regular review of high token consuming workflows can help identify inefficiencies, refine practices, and support collaboration, Gartner says.

Tyagi noted that developers tend to optimize for speed and convenience rather than cost efficiency, so token discipline cannot be achieved through developer choice alone.

His advice for leaders: Do not treat escalating AI coding costs as a reason to move away from AI, or to shift to open generative AI models for everything. “The goal is always to optimize costs without compromising the value.”

Start small, and focus on context engineering first, he said. Assess your current software engineering maturity and select the appropriate agent autonomy. AI assistive development can provide up to 20% productivity gains, “which is not a bad number.”

For developers, he advises: “Target context engineering as one of the most important .