Finance Teams Are Done Flying Blind on AI Costs
The era of “tokenmaxxing,” pushing employees toward the biggest artificial intelligence (AI) models and the heaviest usage as if consumption itself were a sign of progress, is ending after two years of unchecked growth.
That approach worked while AI spending was small enough to absorb without much scrutiny. It no longer is. Enterprise software once ran on annual licenses and seat-based pricing that finance teams could forecast with reasonable accuracy, PYMNTS reported. AI, priced in tokens, compute cycles and application programming interface (API) calls, has broken that model open. New tools are being launched to address the issue.
Ramp launched AI Token Spend Management on July 16, giving finance teams a single dashboard to track, allocate and control AI spending across providers including OpenAI, Anthropic, Gemini and Cursor, Ramp said in its announcement. AI token spend across Ramp’s own customer base increased 20.7x since June 2025, according to the announcement.
Ramp developed the product using usage data from more than 1,300 businesses, and after analyzing 110 trillion tokens, identified three distinct spending profiles based on which models companies chose, how they used caching and how tightly they controlled spend, according to Ramp’s product launch.
Companies Are Shifting From Maximizing Usage to Maximizing Value Per Token
The mechanics of the problem explain why a new software category is forming around it. Unlike seat-based software, AI token spend scales directly with usage and can grow quickly across teams without anyone approving a specific purchase, often sitting behind provider dashboards and invoices finance teams find difficult to interpret.
At one Ramp customer, Sansa Services, an employee left an expensive “Fast Mode” setting active without realizing it, driving up token spend before the mistake was caught. “We flagged that somebody was using Fast Mode unnecessarily and that resulted in six times the token spend over a seven-day period. They’ve since turned it off. That is one story in which basically the product pays for itself,” said Neusha Sayadian, founder and fractional CFO at Sansa Services, Ramp reported on its product page.
As of June, Ramp found that the average business could identify potential savings equal to 12% of its monthly AI spend, and one in three businesses found a lower-cost model alternative capable of doing the same work.
CloudZero is pursuing the same shift from a different entry point. Rather than simply tracking token counts, CloudZero’s financial control platform connects AI spending to the specific customers, features and teams that generated it, aiming to point to what that spending actually produced, CloudZero said in its own launch announcement. “AI is becoming central to how companies build products, serve customers, and run the business,” CloudZero Chief Product Officer Scott Castle said. “But most companies still manage AI with token counts and monthly invoices, even as costs rise faster than expected and ROI remains unclear.”
Finance Teams Want Agents That Manage AI Spending
The demand for this category is showing up directly in how CFOs prioritize agentic AI itself. Dynamic budget reallocation using real-time cost data is the highest-ranked use case for agentic AI among CFOs surveyed by PYMNTS Intelligence, with 43% expecting agents that continuously scan spending patterns, flag overruns and shift funds toward higher-priority areas to have a significant impact. That figure captures the shift underway: finance teams no longer just want visibility into AI spending after the fact. They want a system that actively manages it in real time.
Companies are betting that the next phase of enterprise AI adoption depends less on pushing employees toward heavier AI usage and more on building the financial infrastructure to prove that usage is worth what it costs.
For all PYMNTS AI and digital transformation coverage, subscribe to the daily AI and Digital Transformation Newsletters.
