Pro, PPU or capacity: which licensing model fits your company
A direct comparison by audience and by feature, with the tipping point between per-user licensing and dedicated capacity.
The right question is not price, it is who consumes
Power BI licensing is rarely settled by comparing the price of a single license. It depends on three variables: how many people need to consume content, how many need to create it, and which advanced features your scenario requires.
Start by separating creators from consumers. Creators publish, model and maintain reports; consumers only open them. Companies that license everyone as a creator pay two to three times more than they need to.
Where each model makes sense
Per-user licensing works well while the audience is small and stable — dozens of people, all internal, with no premium feature requirements. It is the simplest to administer and the cheapest way to start.
Premium per user serves teams that need advanced features (more frequent refresh, larger models, deployment pipelines) but still have a limited audience. Dedicated capacity changes the logic entirely: you pay for the environment, not the people, and read-only audience stops driving cost.
The tipping point appears when the number of consumers grows or the audience is external. Past a few hundred readers, capacity is almost always cheaper — and it is the only viable route for distributing reports to end customers, where licensing each user is unfeasible.
The most expensive mistake: licensing without reviewing usage
We often see companies renewing the same contract for years without checking who actually uses it. Licenses for people who left, active test environments and capacity running 24/7 for a morning-only workload are silent waste.
A semiannual usage review — who logged in, how often, which items are orphaned — typically returns 15% to 30% of the cost with zero operational impact.
How we run this decision
We map the real audience, separate creators from consumers, list the features your scenario demands and project cost across all three models. The recommendation comes with numbers, not opinions.
When the scenario involves a broad or external audience, we also present the embedded-reporting alternative, which usually cuts cost per reader dramatically.
We build the cost projection across all three models using your real audience.
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