How to calculate usage-based pricing so as not to lose?

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Atlassian Marketplace partners are moving toward a more flexible pricing future.

RFC-60 opens the door to monetization models based on outputs, consumption, and other usage-linked signals, not just per-user pricing. Whether Marketplace-wide usage-based pricing becomes standard soon, the direction is clear: partners need to plan the adoption of the new features.

The biggest mistake is assuming this is mainly a billing problem.

It is not.

Billing can be built. Packaging can be changed. Pricing pages can be rewritten.

The harder question is this.

What should you actually charge on?

That is where many partners are exposed. They can measure usage, but they cannot confidently tell which usage reflects real customer value, which behaviors predict retention, and which metrics will create trust instead of friction.

That is a usage intelligence problem.

Why seat-based pricing feels blunt

Seat-based pricing is simple, familiar, and easy to administer — but it's often just a rough proxy for value.

For many Atlassian Marketplace apps, value isn't driven by how many users sit in Jira or Confluence. It's driven by what the app helps teams accomplish: automating workflows, processing issues, generating outputs, reducing operational complexity, or supporting mission-critical work.

That's what makes usage-based pricing appealing — it draws a tighter line between price and value delivered.

But it introduces a new risk: if the metric is wrong, the model erodes trust fast. And with the rise of AI agents, billing per seat is heading toward obsolescence.

The most common mistake

Most partners start with packaging questions too early:

• Should we charge per workflow?
• Should we add usage tiers?
• Should we include overages?
• Should we keep seats and layer usage on top?

Those are not the first questions.

The first questions are:

• What behavior best reflects value delivered?
• What usage pattern predicts durable account health?
• What will customers see as fair?
• What metric is predictable enough to budget for?
• What grows with value without punishing adoption?

Usage-based pricing does not fail because billing is hard. It fails because the value metric is weak.

Your billable metric is a foundational decision

Choosing the billable metric is not a packaging detail. It is one of the most important pricing decisions you will make.

It must be:

• Correlated with your costs, so pricing scales with your expenses and protects margins
• Aligned with customer value, so you charge for something that clearly reflects the benefit customers receive
• Good for UX so you do not meter actions in a way that discourages natural product use
• Clear and intuitive so customers can easily understand what they are paying for

If the metric is abstract, hard to predict, or disconnected from how customers experience value, the model becomes harder to defend.

A safer way to transition

If partner want to move toward usage-based pricing without creating avoidable churn risk, they need to do four things well:

• Identify the right value metric
• Model customer impact before launch
• Segment the customer base
• Build visibility into retention, expansion, and churn risk

Solution we use

Journy.io helps Atlassian Marketplace partners turn product usage into pricing intelligence.

That means helping answer questions like:

• What should we charge for?
• Which events reflect monetizable value?
• Which patterns predict retention and expansion?
• Which customers are ready for a usage-based model?
• Where is churn risk hiding inside the transition?

In other words, Journy helps partners move from raw telemetry to pricing readiness.

Final takeaway

Usage-based pricing can be a real growth lever for Atlassian Marketplace partners. It can improve monetization, better align prices with value, and create stronger expansion paths.

But the real risk is not charging for usage.

The real risk is charging for the wrong usage.

The partners who win will not be the ones who move first. They will be the ones who identify the right value metric, model customer impact before launch, segment the customer base carefully, and build visibility into retention, expansion, and churn risk.