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.

The Business Case for Fabric Capacity

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.

Setting Up Your Fabric Capacity Trial

Microsoft offers a 60-day trial period for Fabric capacity—ample time for comprehensive testing. Here's the technical approach we recommend:

1. Initial Setup and Identity Considerations

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.

2. Configure Monitoring Tools

Before migrating workspaces, set up two essential monitoring tools:

• Capacity overage alerts: Configure email notifications for when CU usage exceeds F64 limits (alerts may take up to an hour to trigger)
• Capacity Metrics app: Install this for detailed usage insights—find your Capacity ID in Admin Portal > Capacity settings > Trial tab

3. Incremental Workspace Migration

Rather than moving all workspaces at once, adopt an incremental approach:

• Start with Development: Begin by migrating development workspaces to understand baseline capacity consumption without the risk of taking down Production access
• Test Individual Production Workspaces: Move production workspaces one at a time, starting with those having lower usage and smaller semantic models
• Monitor and Document: After each migration, thoroughly test and document capacity utilization

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.

Load Testing Strategies

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:

Simulating Peak Load

• Open multiple browser tabs with different reports

• Trigger simultaneous semantic model refreshes
• Apply various filters and interactions across reports
• Monitor the capacity metrics for usage spikes

Understanding Capacity Limits

It's important to understand what happens when you approach capacity limits:

• Throttling: Activities begin taking longer to complete
• Request Rejection: If throttling doesn't reduce load sufficiently, the system will start rejecting new requests

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.

Production Validation

After individual workspace testing shows acceptable performance, run a full production test:

1. Migrate all production workspaces to the trial capacity

2. Monitor for 2-3 weeks to capture typical usage patterns

3. Pay special attention to peak usage periods

4. Document CU consumption patterns and identify any bottlenecks

Post-Testing Analysis

Once testing is complete, you'll typically face one of two scenarios:

• Within Capacity Limits: If your production workload runs successfully on the F64 trial, consider purchasing an F64 capacity with annual reservation for best pricing. Remember that developers still need Pro/PPU licenses to publish, and dropped individual licenses remain active for 30 days.
• Exceeds Capacity Limits: Consider sizing up to F128 (double the price and CUs), keeping dev workloads on individual licenses, or using a hybrid approach with efficient workspaces on capacity and others on individual licensing.

Key Takeaways

• Do the Math: Calculate your break-even point between per-user licensing and capacity
• Test Incrementally: Start small and scale up your testing
• Monitor Actively: Use the Capacity Metrics app to track utilization
• Validate in Production: Don't rely solely on dev environment testing
• Document Everything: Build a clear picture of your capacity requirements

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.