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Capacity Monitoring

The Capacity & Cost Monitoring module lets you monitor your Microsoft Fabric and Power BI Premium capacities directly from Soterre PBI Analyzer. When you open this module, you see a dashboard with your active capacities, optimization alerts, and quick-access cards for each capacity.


Before using Capacity Monitoring:

  1. Connect to Power BI Service — you need at least one tenant configured with valid credentials. See Add a New Tenant.
  2. Install Fabric Capacity Metrics app — required for CU consumption data and real-time metrics. See Step 6: Install Fabric Capacity Metrics App.
  3. Provide Metrics Workspace ID and Dataset ID — enter these when adding your tenant. Without them, CU metrics and some alerts will be unavailable.
  4. Add the service principal to the Metrics workspace — at least Member role (Viewer is not enough for DAX queries).
FeatureMinimum License
Basic capacity infoPower BI Pro
CU metrics and alertsFabric Capacity (F SKU) or Power BI Premium (P SKU)

When you open the Capacity Monitoring page, you see:

A dropdown in the top-right corner lets you choose which tenant to view. If you only have one tenant configured, it is selected automatically.

Two cards at the top of the page:

CardWhat It Shows
Active CapacitiesCount of capacities in Active state, with a sub-label showing how many are inactive
Optimization AlertsTotal number of optimization suggestions across all capacities. Turns orange if there are any. Sub-label shows counts of critical and high-priority alerts

Below the summary, each capacity appears as a clickable card showing:

  • Name and a colored state badge (green = Active, orange = Inactive/Suspended, red = Deleting/Deleted)
  • SKU (e.g., F64, P1) and Region
  • Memory Limit in GB with a badge showing the SKU tier (Fabric, Premium, Embedded, Azure, or Trial)

Click any card to open its detail page.

If any suggestions exist across your capacities, they appear below the cards. Each suggestion includes:

  • Severity icon — Critical (red), High (orange), Medium (yellow), Low (blue)
  • Title and description of the issue
  • Recommendation — what to do about it
  • Affected items — up to 3 dataset/workspace names shown as chips

The four types of optimization suggestions:

SuggestionSeverityTrigger
Excessive refresh frequencyMediumAny dataset refreshes more than 8 times per day
Recent refresh failuresHighAny dataset had a failed refresh in the past 7 days
Memory approaching limitMediumMemory usage ≥ 80%
Memory critically highCriticalMemory usage ≥ 95%

If everything is healthy, you’ll see a green checkmark with “Your capacities are running efficiently.”