Why token-based billing is breaking customer trust—and what to do about it.

The rise of AI agents has redefined how users interact with software. Agents run independently, trigger downstream workflows, and consume compute in ways even their creators can’t always predict.

And yet… most pricing systems haven’t evolved to match this reality.

As a result, a growing number of AI-native companies are launching usage-based models that look modern but feel broken—especially to their customers.

Tokens aren’t intuitive. Cost shouldn’t be a mystery.

Token-based pricing makes perfect sense internally. It reflects actual compute usage. It’s measurable. It maps to model cost. Everything your internal teams can see lines up.

But for customers? Tokens are abstract, unpredictable, and often undocumented.

• How many tokens does a given action consume?
• Why does one query cost 10x more than another?
• How do I know if my AI agents are burning through credits?

When customers can’t answer these questions for themselves, they stop trusting your product. Or worse: they stop using it.

Real-world symptoms of pricing breakdown

When pricing goes wrong, there are some clear telltale signs.

• Customers hit usage caps in hours without warning.
• Billing dashboards show vague totals without detail.
• Finance teams have no way to forecast spend.
• Users feel financially punished for using new features.
• Support teams get buried in “Why is my bill so high?” tickets.

We’ve seen this play out with fast-scaling AI companies that assumed they could plug token pricing into an old billing model.

Bad news: You can’t.

Pricing for AI products requires new infrastructure

It may seem like it's just about tokens or other consumption metrics, but the AI-native monetization model is more than that.  It's about transparency, responsiveness, and shared understanding.

To get it right, you need:

• Real-time usage visibility at the feature and agent level
• Custom pricing logic tied to actual product behavior
• User-facing dashboards that show value, not just cost
• Controls and alerts that build trust before a user feels lost

At Metronome, we’ve helped AI teams evolve from vague credit pools to dynamic, user-friendly pricing models that scale with product complexity, not against it.

Your pricing model is part of the product. Make it work like one.

AI is forcing a new contract with your users:

They’ll pay for usage—if they understand what they’re paying for.

The good news? You don’t need to dumb down your pricing.

You just need to make it visible, predictable, and controllable.

About this series

“Pricing Is a Product” is a multi-part series exploring how modern teams are rethinking monetization. From real-world missteps to system design principles, we’ll break down how to build pricing models that are visible, predictable, and built for scale.

Up next: Pricing Experiments Shouldn’t Break Finance.