AI agents have moved from experimental pilots to permanent fixtures in modern customer service teams. They’re resolving inquiries faster, scaling across channels, and helping companies cut costs without sacrificing the customer experience.

But there’s one area where clarity still lags behind: pricing .

As businesses get serious about AI customer service, they’re asking: How should we be paying for this? And more importantly: What are we actually paying for?

Most vendors today offer one of two models: resolution-based or conversation-based.

At first glance, both sound fair. But dig deeper and the differences become more than technical, they shape how you scale, what you can measure, and how your ROI unfolds over time.

This post breaks down the two models, where each one falls short or stands out, and a clear winner.

why AI agent pricing feels more complex than it should

Pricing confusion in this space isn’t accidental. It’s a legacy of the human agent era, where cost was tied to headcount—seats, shifts, and hours worked.

AI flips that logic. Now, we’re measuring outcomes instead of effort. But many pricing models haven’t evolved to reflect that shift. Some vendors still use fuzzy definitions to inflate performance metrics, and others hide complexity behind language that seems clear, but rarely is.

That lack of transparency makes it hard for buyers to compare vendors, or to forecast what success will actually cost them. So let’s simplify things.

the two pricing models that matter

You’ll see different labels in the market, but nearly every AI agent platform pricing model boils down to one of these:

1. Resolution-based pricing: You’re charged every time your AI agent “resolves” a conversation. The logic here is performance-based: you only pay for results. But the catch is how “resolution” is defined. More on that soon.
2. Conversation-based pricing: You’re charged based on how many conversations your AI agent handles, regardless of outcome. It’s a simple consumption model that measures usage, not performance.

At face value, resolution-based pricing seems like the more efficient model. But in reality, that promise often falls apart when you put it into practice. Here’s how to evaluate the two models using four key decision criteria.

1. definitions and mechanics

Resolution-based

The term “resolution” isn’t standardized. One vendor might define it as there being no customer response for 5 minutes. Another might consider any interaction that didn’t escalate to a human agent as resolved. But did the customer leave satisfied? Did they leave at all?

These metrics often fall into the "containment trap” or counting disengagement as success. That’s not a resolution. That’s abandonment with a different label.

Conversation-based

Conversation-based pricing doesn’t require interpretation. A conversation is a conversation. You pay for what your AI handles, not how it’s labeled. This makes comparisons easier and billing more honest.

Bottom line: Resolution-based pricing is inconsistent. Conversation-based pricing is standardized.

2. ability to validate charges

resolution-based

If you’re paying per resolution, you need confidence that a real resolution happened. That often means auditing transcripts, setting up manual reviews, or reconciling data across systems. It’s time-consuming, and the stakes are high if you find inaccuracies.

Mislabelled resolutions can cost teams twice: once when you pay the AI to “resolve” it, and again when a human ends up fixing it through another channel.

Conversation-based

Validation is simple. You count conversations. There’s no dispute, no back-and-forth with vendors, and no time lost second-guessing what’s on your invoice.

Bottom line: Resolution-based pricing costs time and effort to verify. Conversation-based is transparent by design.

3. predictability of costs

Resolution-based

Cost = volume x resolution x price per resolution. Sounds good—until your automation improves and suddenly you’re paying more for the same number of conversations. The model punishes success.

This unpredictability makes budgeting difficult, especially for teams investing in long-term automation.

Conversation-based

Cost = volume x price per conversation. Straightforward, scalable, and easy to model out for the year ahead.

Bottom line: Resolution-based pricing introduces hidden volatility. Conversation-based pricing is easy to forecast.

4. scalability and incentive alignment

Resolution-based

Here’s the paradox: as your AI agent gets better, your costs go up. Higher resolution rates mean more “success” but also more dollars spent. That misalignment can stall progress and cannibalize ROI.

Conversation-based

With conversation-based, better performance equals more value per dollar. You resolve more inquiries, contain more volume, and reduce human workload, without being charged extra for being good at it.

Bottom line: Resolution-based pricing punishes performance. Conversation-based pricing rewards improvement

Let’s say you handle 500,000 conversations in Year 1 and expect 10% annual growth. Your AI agent starts with a 25% resolution rate and improves to 75% over three years. You’re comparing:

• Resolution-based pricing: $1.50 per resolution
• Conversation-based pricing: $0.35 per conversation

By year three, you’re paying more than triple with resolution-based pricing—just because your AI agent got better at its job.

so… is resolution-based pricing bad?

Not at all. Measuring resolutions can be incredibly useful. It helps teams understand what’s working, where deflection is happening, and how much volume your AI agent is truly resolving.

But as a pricing model, it creates risk, complexity, and costs that scale in the wrong direction. Resolution is how you measure performance. Conversation-based pricing is how you scale it.

choose pricing that works for you—not against you

AI agent pricing shouldn’t feel like a gamble. You deserve a model that grows with your automation maturity . One that gives you visibility into what you’re paying for, confidence in your data, and room to improve without blowing up your budget.

Conversation-based pricing checks all the boxes:

• Transparent billing
• Predictable costs
• Easy validation
• Aligned incentives

And best of all, it creates the space for your AI agent to become what it should be: your #1 customer service employee , not a hidden cost center.