What Even Is a Credit? The Hidden AI Tax Inside Usage-Based Pricing

The AI Tax bluntly arrives at the front door of your next renewal.

It shows up as a line-item increase at renewal. Fifteen percent. Twenty percent. Thirty percent even. According to Tropic data, these AI uplifts tend to sit between 20-37%. Vendors point to "AI-powered features," bundle them into existing products, and move customers into new SKUs whether they asked for them or not.

But there’s another part to this tax that can easily slip through the back door - the gap between what usage-based pricing promises and what buyers can actually measure, forecast, and control.

Usage-based pricing models (via credits) have become the standard for AI. In fact, credit-based pricing exploded 126% year-over-year in 2025. Among the top 500 SaaS and AI companies, there were more than 1,800 pricing changes in a single year - an average of 3.6 pricing changes per company. Some vendors changed their pricing every single month.

It’s a chaotic shift in software pricing…and buying.

Usage-based pricing built on credits, agents, and outcomes promise flexibility and do deliver it (most of the time). But they also introduce a subtler problem: most finance leaders can't clearly explain what they're paying for anymore. The ROI to justify this spend is unclear.

When the Bill Makes Sense (Until It Doesn't)

Tropic CFO Russell Lester recalls moments where he'd been actively using AI for hours - experimenting, iterating, getting value - and then stopped to ask himself simple questions like:

• How many agents am I using right now?
• What exactly am I paying for? To what level?

This is the paradox at the heart of AI pricing today.

The tools are intuitive, powerful, and immediately useful. But the economics behind them? Anything but intuitive. The more "agentic" software becomes (the more autonomous actions it takes on your behalf), the harder it becomes to map usage to dollars in real time. It proliferates over:

• Overages that trigger automatically without warning
• Credit prices that creep up quietly at renewal
• Consumption that grows without clear business outcomes
• Finance teams who can't explain the bill to leadership

Let's Give The Benefit of The Doubt: This Isn't Malicious

It's tempting to frame this as vendors deliberately obscuring costs. But that's both wrong and unhelpful.

Usage-based pricing for AI exists because AI fundamentally breaks traditional seat-based pricing models.

Traditional software seats don't map cleanly to value when one user can generate 10x more AI output than another, when usage spikes unpredictably based on business cycles, when infrastructure costs fluctuate based on compute demands and model inference. 

From a vendor's perspective, credits and consumption-based pricing models are often the only viable way to recover massive AI investments without catastrophically mispricing power users.

From a buyer's perspective, those same models unlock real benefits:

• Faster experimentation
• Less shelfware
• Easier pilots
• Lower upfront commitment

Tropic COO Justin Etkin explained that vendors are under enormous pressure to prove AI monetization - and usage-based pricing is often the most defensible path forward.

"If leading software providers can't show AI revenue uplift, the whole AI ecosystem starts to fall apart very quickly."

So no, this isn't a story about bad actors. It's a story about two sides trying to price something fundamentally new, in a market that hasn't stabilized yet.

And the problem isn't that credits exist. It’s just that they are pretty abstract with varied definitions right now.  

What Is a Credit? The New Economic Unit That’s Hard to Define

In theory, an AI credit is just a unit of consumption - a way to measure how much of a service you're using.

In practice, AI credits are a proxy for many different things at once:

• Compute usage and processing power
• Inference calls to language models
• Actions taken by autonomous agents
• AI agents deployed across workflows
• Outcomes delivered (meetings booked, tickets resolved)

These units are rarely standardized or intuitive across vendors.

"What is a credit? Can you measure it? How does one AI interaction consume credits versus another? All of this stuff is super murky right now." – Justin Etkin

If your procurement and finance teams can't articulate what a credit represents, which actions consume it, and why usage varies across similar behaviors, then you're sort of budgeting without a unit of measure.

Buyers are left navigating difficult questions about AI credit pricing that affect planning and forecasting:

• Why does one AI interaction cost more credits than another?
• What actually counts as "usage" in consumption-based models?
• How do pooled credits mask where AI spend is coming from?
• What happens to credit pricing when models or features change mid-contract?

And when finance teams can't answer them, they lose the ability to forecast AI costs, govern consumption, and negotiate effectively. That's when flexibility starts to feel less like freedom.

When AI-Driven Spend Expands Faster Than Visibility

Russell described the fundamental mismatch between how AI pricing works and how budgets actually get approved:

"CFOs are not sitting there saying, 'Oh, it's budget season - everyone just add 25% to your software stack.' The math doesn't math."

When AI-driven spend grows faster than budgets, something breaks. Scrutiny increases at renewal. Pressure mounts to consolidate tools. Trade-offs get harder. Vendors face skepticism about vague innovation premiums.

The risk isn't a one-time uplift. Its compounding increases layered on top of opaque usage - AI-justified price changes that stack year over year while visibility remains poor.

Eventually, the reckoning arrives for your tools: “All of you can't be asking for this uplift,” says Russell. “One of you has to go."

And here's the more dangerous dynamic: spend that grows without anyone deciding it should.

Russell described using AI tools extensively and getting real value, experimentation, and iteration, but being unable to tell, in the moment, what that usage translated to financially. 

When consumption expands through broader adoption, more autonomous agents, or heavier experimentation, teams don't decide to spend more. They discover later that they already have.

When usage can expand without explicit approval, spend becomes reactive rather than intentional. Pricing models designed to align spend with value can accelerate tool rationalization when finance can't see what's driving costs. Every vendor becomes suspect.

This is the visibility crisis that Russell describes: do companies really know how much they’re investing in AI right now?

When usage data is delayed, aggregated, or difficult to attribute by team or function, finance teams lose the ability to connect behavior to cost. Without that connection, forecasting becomes guesswork. And that's where the AI Tax truly lives - not in the price increase you negotiate, but in the consumption you can't see coming and track properly.

Entropy and Why Things Get Messier, Not Cleaner

Here’s another thing Russell describes: in physics, entropy describes the tendency of systems to move toward disorder over time. It's easy to melt an ice cube. Much harder to put it back into its original shape.

AI adoption follows the same pattern. "Things don't tend to get cleaner and more organized. They tend to get messier,” says Russell.

More models. More tools. More pricing schemes. More invoices with line items you can't decode.

Usage-based pricing accelerates experimentation faster than governance can keep up. The result is way more complexity, which is where hidden costs thrive.

Think about your own tech stack right now:

• How many AI tools are you paying for via credits?
• Can you forecast next quarter's consumption with confidence?
• Do you know which departments are driving your highest usage?
• What happens when someone spins up an AI agent and forgets to turn it off?

What to Do About It: Managing Credit-Based AI Pricing

The most effective teams aren't rejecting usage-based AI pricing. That’s unrealistic and unreasonable. 

They're matching flexibility with discipline - insisting on definitions before scale, aligning usage growth with outcomes rather than hype, and retaining the ability to change course when value doesn't materialize.

You need to bring financial discipline to AI consumption and credit allocation.

1. Demand Shared Definitions of AI Credits Before You Sign

Before committing to any credit-based pricing model, get specific answers in writing:

• What exactly is a credit in your pricing model?
• How is credit consumption calculated?
• What AI actions or interactions consume how many credits?
• How do AI credit prices change at renewal?
• What changes trigger re-metering of credit usage over time?

The goal isn't micromanagement. It's basic financial literacy applied to AI consumption. If the vendor can't provide clear, written definitions of their credit structure, that's a red flag. Push back: "I can't budget for AI costs I can't measure."

2. Build Consumption Guardrails Into AI Contracts

Usage-based AI pricing requires explicit consumption controls to prevent budget overruns. Rather than arguing against AI investment altogether, introduce guardrails around how both usage and pricing can evolve:

• Monthly usage caps that trigger alerts before overages occur (not automatic upgrades)
• Credit price locks that cap how pricing changes over time, separating usage growth from price growth
• Transparent consumption reporting delivered monthly with clear attribution by team or function
• Downgrade rights if AI credit consumption drops below committed minimums
• Pre-defined overage protocols that clarify how excess usage is handled before it happens

One gotcha to watch: some vendors auto-upgrade you when you hit credit usage limits. You can easily scale up but can't scale back down until renewal. That's a budget disaster waiting to happen. Negotiate bilateral flexibility - if you can go up, you should be able to go down.

This is about ensuring that spend more is conscious, not accidental.

3. Treat AI Credits Like Real Money in Your Budget

Set up the same governance you'd apply to any other budget line for credit-based tools. In a time where AI usage can shift daily, end-of-cycle reporting simply isn't sufficient:

• Monthly variance reports comparing contracted vs. actual AI credit consumption
• Department-level visibility into who's consuming credits and why
• Earlier signals when consumption trends change, not retrospective summaries
• Quarterly reviews to assess value delivered vs. AI costs incurred

As Justin noted, AI-native tools have one major advantage over traditional SaaS: "You can turn these tools on quickly - and you can turn them off just as quickly. That's a huge shift."

That flexibility becomes leverage - but only if you use it intentionally. If you can't demonstrate ROI within 90 days, be willing to pull the plug.

Dig In: Have Better Conversations About AI Credit Pricing With Vendors

Here's what better conversations about credit-based AI pricing sounds like:

1. Instead of: "How much will this cost?" Ask: "Walk me through exactly how AI credits are calculated based on usage scenarios and what each would cost annually based on consumption.”
2. Instead of: "Can we pilot this?" Ask: "Can we structure a pilot with fixed credit allocation, clear success metrics, and the right to walk away after 90 days with no penalty if credit-based pricing doesn't deliver value?"
3. Instead of: "What happens if we exceed our credit commitment?" Ask: "What controls exist to prevent runaway AI credit consumption? Can we set hard caps on credit usage? Do you offer tiered pricing with predictable uplift thresholds?"

The best vendors won't resist these questions about AI credit pricing - they'll welcome them. They're confident their tools deliver value and want customers to see it clearly.

Vendors who deflect, use vague language, or claim "credit-based pricing is too complex to explain simply"? Those are the ones charging premiums that aren't justified by transparency.

The Bottom Line on AI Credit Pricing

Usage-based AI pricing may be the best imperfect option the market has right now. It gives vendors a path toward sustainability. It gives buyers flexibility they've never had before.

But you have to manage the uncertainty with financial discipline and make AI costs as legible as you can. Insist on:

• Shared definitions of what AI credits represent
• Real-time visibility into who's consuming credits and why
• Boundaries that protect budgets without killing innovation
• Clear alignment between credit consumption and business outcomes

Try this as your next step: Pull your last three months of invoices for any AI tool priced on credits or agents. Can you explain what you paid for? If not, that's your starting point for bringing transparency to AI-driven spend.

Are you ready for this AI Tax? Check out more of what Russell and Justin outline in our podcast episode.