Posted by Eric Krapf on 18 June 2026
Can We Find AI Pricing Models That Work?
AI pricing is suddenly a hot topic for enterprises. Coders and even “civilian” end users who were encouraged to “tokenmaxx” in search of their enterprises’ killer AI apps are being told to dial back usage as CFOs confront the unpredictability of AI costs under current pricing schemes. Basically, giving users unlimited access to a service with metered pricing turned out to be a recipe for overspending.
The industry seems aware that it has a problem on its hands. Oracle is responding by testing a model of selling AI tokens in bundles, hoping to give enterprises more control and predictability around their spend. A new industry group, the Tokenomics Foundation, has been formed to study usage and pricing models in an attempt to, as the name suggests, create viable models for tying price structures to business value.
One challenge for enterprises is that their end users have spent most of their careers, if not most of their lives, approaching technology challenges from the perspective of abundance, rather than scarcity. Bandwidth may never have become too cheap to meter, as early Internet gurus promised, but the approach of “throwing bandwidth at the problem” was often a feasible way to make networks and applications work. The industry was riding high on Moore’s Law.
As capital-intensive as the buildout of the high-bandwidth Internet might have been, it was undertaken by companies that, in most markets, competed within a duopoly – phone companies vs. cable. While they needed to try and satisfy the insatiable demand for bandwidth, they weren’t subject to the kind of free-for-all that we’re seeing with the big AI players. That affects the providers’ strategies in earning back their investments.
If you want to understand how the current pricing issues are playing out in UCaaS and CCaaS services, you’ve got to read this in-depth piece by Kevin Kieller on No Jitter. Kevin provides some great details on the ways that UCaaS/CCaaS players are experimenting with pricing models for their AI services, then he looks at the broader issues enterprises must confront as they think about AI pricing.
Kevin gets to the heart of the issue, which is the tension between what enterprise communications folks have been told (especially for contact center services), and what vendors must do to earn back the staggering costs they’re incurring to implement AI:
“The value proposition for AI is often positioned as a 7x24x365 worker that can tirelessly do infinite work. Create more documents. Summarize more meetings. Handle more customer interactions. Generate more responses. Build more slides. Analyze more data. Automate more processes,” Kevin writes.
“But if every additional unit of output creates additional cost, organizations may become reluctant to use AI for exactly the work it is supposed to accelerate.
“That is not how we usually think about human productivity. If a talented employee produces more high-quality work, we celebrate it. If an AI teammate produces more high-quality work, we may need to check whether we can still afford it!”
It’s a paradox I cited a few weeks back regarding a report on runaway costs for AI coding. You’re not necessarily wasting money on things that don’t work; you’re “wasting” it on things that may work, but may turn out to be a more expensive way to achieve the desired result. The enterprise has to ask itself which resource really is most cost-efficient for generating the business value they’re after: The AI-driven metered model, or the human-driven fixed-cost model? As Kevin points out, that goes not just for those building AI-driven technology internally, but those employees (like contact center agents) who could potentially be replaced by AI.
The good news is that the industry, led by the giant AI providers, seems to have been quick to acknowledge the problem and need for solutions. The bad news is, the solutions that could satisfy the needs of both the AI providers and their customers are far from clear at this point.
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