As organizations scale their Power BI deployments, the rising costs of per-user licensing can quickly become a significant budget concern. With Microsoft's recent price increase of $4 per user for both Pro ($10 to $14) and Premium Per User ($20 to $24) licenses, many companies are exploring Microsoft Fabric capacity as a cost-effective alternative. While Microsoft provides a Fabric capacity calculator to guide SKU selection, our experience shows that nothing beats hands-on testing in your actual environment.
For organizations with hundreds of Power BI users, the economics of Fabric capacity can be compelling. Take, for example, a recent client with 300 users requiring Power BI access. At $24 per Premium Per User license, their monthly cost would exceed $7,200. By contrast, an F64 capacity—the first tier offering unlimited read access—costs approximately $5,000 per month. Add 20 Premium Per User licenses for developers, and the total monthly savings approach $2,000, or $24,000 annually. However, determining whether an F64 capacity can handle your workload requires more than simple arithmetic. It demands rigorous testing.
Microsoft offers a 60-day trial period for Fabric capacity—ample time for comprehensive testing. Here's the technical approach we recommend:
The person who initiates the trial capacity must also be the one to set up the Capacity Metrics app initially. This identity-based requirement isn't immediately obvious but is crucial for monitoring capacity utilization during testing. Each user can start one F64 trial by default—if you're unable to start a trial, check the "Users can try Microsoft Fabric paid features" setting in the Admin Portal.
Before migrating workspaces, set up two essential monitoring tools:
Rather than moving all workspaces at once, adopt an incremental approach:
The migration itself is straightforward—simply edit the workspace license info from Premium Per User to Trial Capacity. No data movement is required, though switching from Premium Per User requires re-running semantic models.
While automated testing tools like Selenium or Playwright can simulate concurrent user activity, manual testing can be sufficient for smaller environments. Our testing approach includes:
Open multiple browser tabs with different reports
It's important to understand what happens when you approach capacity limits:
Both front-end activities (report interactions) and back-end processes (semantic model refreshes, data pipelines, lakehouse operations) consume capacity units (CUs), so test both simultaneously. Brief periods where CUs are exceeded aren't concerning—the system can borrow ahead and recover before impact. You can read more about capacity overages here.
After individual workspace testing shows acceptable performance, run a full production test:
Migrate all production workspaces to the trial capacity
Monitor for 2-3 weeks to capture typical usage patterns
Pay special attention to peak usage periods
Document CU consumption patterns and identify any bottlenecks
Once testing is complete, you'll typically face one of two scenarios:
Microsoft Fabric capacity can deliver significant cost savings for organizations with large Power BI user bases. However, successful implementation requires methodical testing and validation. By following a structured testing approach during the trial period, you can confidently determine the right capacity SKU for your organization and avoid both under-provisioning and overspending.
Need help evaluating Microsoft Fabric capacity for your organization? Our data and analytics experts can guide you through the testing process and ensure you make the right capacity decisions for your specific workload requirements.