Token consumption can be significant when it’s human-to-AI interaction, but with agentive AI, token use increases considerably because tokens are used every step of the way in a process, said Dave Salvatore, director of accelerated computing products in the Accelerated Computing Group at Nvidia.
“Not only do you know you need the fast throughput, but you need the fast response time, because ultimately that end-to-end operation is going to define how long it takes you to get back your full answer,” he said.
How Tokens Drive Enterprise Decisions
Tokens are used as the currency for public AI products, such as text to image or image to video conversions. Within an enterprise, this situation is different. Token consumption is commonly expressed in cost per million tokens.
In enterprise settings, this often translates to two approaches: metered usage models, where departments or applications consume tokens against a defined budget; and enterprise or site licenses, where organizations negotiate volume-based pricing to manage costs at scale.
Some enterprises may allocate token budgets to departments, setting soft or hard limits to control usage. Others may rely on centralized licensing to simplify governance and cost management. Either way, tokenomics becomes a core part of financial planning for AI initiatives.
