Agentic AI Inference Costs Set to Balloon Over 5X by 2028 as Complexity Outpaces Savings

Agentic AI costs set to balloon fivefold by 2028

The cost of agentic AI workflows is forecast to increase more than fivefold by the end of 2028 as users adopt more complex applications of the technology. As Nvidia and other tech giants push inference and agentic AI as the next stage of the AI wave, Gartner warns that the cost of implementing these systems will rise even as foundation models become cheaper. The analyst firm has cast its eye over the nascent world of AI agents - systems designed to act independently in pursuit of a goal - and sees multiple challenges ahead. Leaving aside the substantial security concerns, these software agents are considerably more complex than chatbots. Gartner believes falling model prices are tempting users to build more complex workflows, whose greater token consumption can outweigh those savings and drive up overall inference costs. In other words, tokens are becoming more cost-efficient, but those savings are not keeping pace with the rising cost of more advanced AI capabilities. The rate of innovation is outpacing the cost curve, Gartner claims. "The harsh economics of the inference paradox are exemplified by the differences between a simple chatbot and an AI agent," says Gartner senior director analyst Will Sommer. "Where a simple chatbot must read and interpret a query and quickly respond with a probabilistic reasonable answer, an AI agent must constantly reason, negotiate, and question itself," he explains. Those processes add up: routing a task to an agentic reasoning model increases inference costs at least fivefold, and potentially by much more as the task becomes more complex. Securing a return on investment from such advanced AI tools therefore demands either much greater returns than basic models provide or better optimization of inference, routing, and orchestration, Gartner warns. This could mean assigning each task to the most cost-efficient model capable of handling it. The move by some AI providers from flat-rate subscriptions to usage-based billing hasn't helped, as The Register reported last month. Token-heavy workflows can produce runaway costs under the new model. Perhaps it is no wonder Gartner predicted earlier this year that 40 percent of organizations would demote or decommission AI agents because of problems with the heavily hyped technology. The analyst biz also cheerily forecast last year that at least half of all generative AI projects would blow their budgets because of poor architectural choices and a lack of expertise, while most attempts to build custom models would be abandoned. ®

AI Inference Costs per Agentic Workflow to Surge Over 5X by 2028, Gartner Predicts

Product Leaders Are Facing the Inference Paradox: Better Unit Economics Is Escalating the Overall Cost of AI Without Providing a Clear Pathway to Commensurate and Predictable Value AI inference costs per agentic workflow will increase more than fivefold through 2028 according to Gartner, Inc., a business and technology insights company. As AI products evolve from assistive features to multistep execution, product leaders face a new margin challenge with falling model prices subsidizing more complex workflows and escalating total AI costs. As a result, inference cost management has become a top priority for product leaders. "Product leaders cannot rely on more efficient token economics to rationalize AI costs," said Will Sommer, Sr. Director Analyst. "Each successive generation of AI capability will necessitate more, and often more expensive, tokens. There is no reliable, economical one-size-fits-all model on the horizon. Producing competitive AI products will require developing and maintaining complex multimodel ecosystems." Gartner has identified three fundamental trends driving token economics: * Foundational model cost economics are rapidly improving. * Improved AI efficiency is unlocking the deployment of more powerful, and more expensive, models to enable higher-value, more sophisticated AI applications. * More sophisticated AI workflows use far more tokens than simple chatbot interactions, driving higher overall inference costs. These dynamics mean that tokens are becoming more cost-efficient, but not as quickly as AI capabilities and the costs associated with those capabilities are increasing (see Figure 1). The rate of innovation is outpacing the cost curve. Figure 1: AI Complexity Outpaces Falling Token Costs Source: Gartner (August 2026) This is the Inference Paradox, defined as better unit economics escalating the overall cost of AI without providing a clear pathway to commensurate and predictable value. "The harsh economics of the Inference Paradox are exemplified by the differences between a simple chatbot and an AI agent," said Sommer. "Where a simple chatbot must read and interpret a query and quickly respond with a probabilistically reasonable answer, an AI agent must constantly reason, negotiate, and question itself." All of these responsibilities add up. Compared to a basic chatbot interaction, routing a task to an agentic reasoning model increases provider inference costs by at least five times, and often much more as task complexity grows. Ensuring ROI from advanced AI, like reasoning agents, demands exponentially higher returns relative to basic models, or highly optimized inference-tiering, routing and orchestration to calibrate complex tasks relative to more cost-efficient intelligence. Both of these outcomes are eminently possible but will require significant effort across complex workflows. "Defaulting to generic autonomous intelligence will result in unbounded costs orders of magnitude higher than those of optimized product ecosystems," said Sommer.