Techno Blogging
AI inference costs per agentic workflow will increase more than fivefold through 2028, according to Gartner, as AI products evolve from assistive features into multistep, autonomous execution.
The firm said product leaders now face a new margin challenge, with falling model prices subsidizing increasingly complex workflows and driving up total AI costs, making inference cost management a top priority. "Product leaders cannot rely on more efficient token economics to rationalize AI costs," said Will Sommer, senior director analyst at Gartner. "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 identified three fundamental trends driving token economics: foundational model cost economics are improving rapidly; that improved efficiency is unlocking deployment of more powerful and more expensive models for higher-value applications; and more sophisticated AI workflows consume far more tokens than simple chatbot interactions, driving higher overall inference costs. Together, the firm said, these dynamics mean tokens are becoming more cost-efficient, but not as quickly as AI capabilities and their associated costs are rising — a pattern Gartner said shows the rate of AI innovation outpacing the cost curve.
Gartner calls this dynamic the "Inference Paradox," defined as better unit economics escalating the overall cost of AI without providing a clear pathway to commensurate, predictable value.
Sommer said the paradox is best illustrated by comparing a basic chatbot to an AI agent. "The harsh economics of the Inference Paradox are exemplified by the differences between a simple chatbot and an AI agent," he said. "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."
According to Gartner, routing a task to an agentic reasoning model increases provider inference costs by at least five times compared with a basic chatbot interaction, and often significantly more as task complexity grows. The firm said ensuring return on investment from advanced AI such as reasoning agents will require either exponentially higher returns relative to basic models, or highly optimized inference-tiering, routing and orchestration to match task complexity against more cost-efficient intelligence — both achievable outcomes, Gartner said, but ones that demand 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," Sommer said.
The firm said product leaders now face a new margin challenge, with falling model prices subsidizing increasingly complex workflows and driving up total AI costs, making inference cost management a top priority. "Product leaders cannot rely on more efficient token economics to rationalize AI costs," said Will Sommer, senior director analyst at Gartner. "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 identified three fundamental trends driving token economics: foundational model cost economics are improving rapidly; that improved efficiency is unlocking deployment of more powerful and more expensive models for higher-value applications; and more sophisticated AI workflows consume far more tokens than simple chatbot interactions, driving higher overall inference costs. Together, the firm said, these dynamics mean tokens are becoming more cost-efficient, but not as quickly as AI capabilities and their associated costs are rising — a pattern Gartner said shows the rate of AI innovation outpacing the cost curve.
Gartner calls this dynamic the "Inference Paradox," defined as better unit economics escalating the overall cost of AI without providing a clear pathway to commensurate, predictable value.
Sommer said the paradox is best illustrated by comparing a basic chatbot to an AI agent. "The harsh economics of the Inference Paradox are exemplified by the differences between a simple chatbot and an AI agent," he said. "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."
According to Gartner, routing a task to an agentic reasoning model increases provider inference costs by at least five times compared with a basic chatbot interaction, and often significantly more as task complexity grows. The firm said ensuring return on investment from advanced AI such as reasoning agents will require either exponentially higher returns relative to basic models, or highly optimized inference-tiering, routing and orchestration to match task complexity against more cost-efficient intelligence — both achievable outcomes, Gartner said, but ones that demand 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," Sommer said.
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