Surging AI costs could exceed developer salaries by 2028 – analysts say context engineering could be the key to optimizing token consumption

AI coding costs could exceed software developer salaries by 2028, according to new research from Gartner, prompting calls for a greater focus on cost optimization efforts.

The projection by the consultancy is a result of two overlapping trends, including surging token consumption rates and the shift to consumption-based pricing (CPB) models.

A host of software and AI providers have pivoted away from flat rate “per-seat” subscriptions in recent months to CPB setups. As ITPro reported in April, GitHub signalled its own shift on this front, citing rising costs specifically as a key factor behind the decision.

Speaking to ITPro, Nitish Tyagi, Senior Principal Analyst at Gartner, said conversations with clients shows this is rapidly becoming a key concern for enterprises, particularly software engineering leaders as teams ramp up adoption of tools.

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“What has happened is that most vendors have switched to a consumption-based pricing model, and it took most engineering organisations by surprise,” he said. “We were never thinking that AI will be this costly, and we are already seeing that cost.”

“Initially, it wasn't alarming,” Tyagi added. “While many organizations are still in the range of $200 to $500 per developer per month, alarming results are coming up.”

Tyagi noted that discussions with Gartner clients show heightened use of AI is costing more than $2,000 per developer, per month. These costs are rising as well, with some eye-watering figures now emerging.

“I’ve been talking to clients where they are telling me that my power users are now costing me more than $2,500 per developer, per month. Sometimes we also hear really crazy numbers, like ‘my developer cost me $20,000 last month, or my business users cost me $32,000 per month.”

Reports on surging AI costs have been coming thick and fast in recent months, and some major firms such as Microsoft have gone so far as to implement usage limits or even cut the use of specific tools internally.

As ITPro reported earlier this month, Uber blew through its entire annual AI budget in just four months after encouraging staff to ramp up their use of AI tools internally.

One of the leading causes behind these price increases and hefty bills lies in ‘tokenmaxxing’ – a trend in which enterprises measure AI usage based on the number of tokens consumed by users.

For some, it’s a way of tracking productivity by highlighting their use of the technology, although critics argue this pads stats and results in skewed metrics.

These overlapping trends raise serious concerns about long-term AI usage, particularly around returns on investment (ROI). Simply put, enterprises want their devs using AI, but heightened costs mean that measuring value is becoming more difficult.

“This is becoming a big issue from two aspects,” he told ITPro. “It’s not only related to developer salaries. This will catch the eyes, but the bigger problem here is that organizations were already struggling and justifying ROI for using these tools.”

“Now that the cost has increased, and it is increasing as well, it is becoming even more difficult to justify these costs and to identify where the ROI is.”

Cost optimization practices need to improve

Tyagi is keen to emphasize that the study doesn’t suggest that developers drop AI tools or agents outright. This completely misses the point, he said.

Instead, engineering leaders and enterprises at large need to sharpen up on cost optimization processes. Efforts on this front are critical, particularly as the study noted that vendors themselves are “yet to deliver mature, built-in cost optimization capabilities” for agents.

A key practice highlighted by Tyagi includes context engineering. This is a technique that involves selectively curating, structuring, and managing information used by AI systems to generate responses.

This essentially involves providing only the relevant information needed by agents, as well as concise summarization of content and elimination of "unnecessary data”

Tyagi noted that this will not only help optimize token consumption for developers, but ultimately deliver longer term benefits in terms of output quality.

“Context engineering is going to become the most important skill in the future,” he told ITPro. “If I actually optimize token consumption, the I would be able to increase the output quality as well.”

This is an emerging focus area for software engineering leaders, Tyagi added, and Gartner suggests that context engineering practices could eventually be mandated to cut consumption rates.

It’s also a blossoming market, he noted. Tabnine, for example, recently launched a new solution known as “Context Engine” aimed at streamlining processes on this front while Atlassian is also developing solutions in this domain.

Selective use

Gartner’s research also urges enterprises to establish a “use-case-driven decision” framework when it comes to using AI for everyday tasks.

Simply put, the focus here should be on clearly defining when AI coding agents should be used, and how much autonomy these bots are given on particular tasks. Given the increasing use of agents among developers, many are simply allocating tasks that aren’t needed.

“Not everything has to be done by an agent, not everything has to be done by developers,” he said.

Looking ahead, developer teams should assess when - and when not to - assign agents to specific tasks, which Tyagi noted could help cut needless consumption and ultimately provide greater control.

“When you’re working on highly sensitive tasks or highly complex tasks, then you want your developers to break down those tasks as well into smaller sub tasks,” he explained.

“We have seen when you are throwing smaller clues, problems at agents, agents do much better than giving them an open-ended problem,” Tyagi added. “Agents are improving, but I think developers should have that level of control right now, through which they can actually optimize the token consumption.”

It’s here that model selection is equally important, according to Gartner, specifically for smaller tasks, which can naturally be handled by smaller models.

Using “intelligent model routing” strategies will enable developers to box clever when it comes to smaller, niche AI models or larger frontier options, which typically come with added costs.

The consultancy noted that escalation is needed “only when complexity demands it”.

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