“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.
“Selling” tokens to your employees may seem strange, but Salvadore said employees will not be given a blank check to use enterprise AI applications or public AI services. “[IT] has to think about how can we do this in a way that’s going to get our organization the capabilities that they need to really make a good use of authentic AI, while at the same time balancing the ability for individual users to be able to get what they need quickly enough, while also balancing cost,” he said.
Virtually all of the public API services providers offer tiered usage. For example, ChatGPT has four pricing plans, from free to Pro, which runs for $200 per month but offers considerably more services than the free version. Through tokenomics, enterprises can buy Pro-level services but limit their use or availability.
