What causes Capacity is overloaded?

Multiple large datasets are refreshing simultaneously, exhausting available memory. A single dataset with a very high cardinality causes memory pressure for all other operations. Report queries from many concurrent users are competing with background refresh operations. The capacity SKU is undersized for the number of workspaces and datasets it hosts. Auto-scale is not enabled and capacity spikes cannot be handled by the base SKU

How do I fix Capacity is overloaded?

Open the Premium Capacity Metrics app to identify which workspaces and datasets consume the most CPU and memory.. Enable capacity auto-scale (if available in your region) to handle peak loads.. Stagger refresh schedules to avoid simultaneous peaks — spread refreshes across different hours.. Move large or frequently-refreshed datasets to a dedicated capacity to isolate their resource usage.. Enable incremental refresh for the largest datasets to reduce per-refresh memory requirements.

High severitycapacity

Power BI Refresh Error:
Capacity is overloaded

What does this error mean?

The Power BI Premium capacity hosting this workspace has run out of resources. Refresh jobs are queued, throttled, or rejected when capacity CPU or memory is saturated. When capacity is severely overloaded, Power BI may not start a refresh at all — no failed run appears in the refresh history, and the schedule silently falls behind. Track this via the Premium Capacity Metrics app alongside your refresh monitoring tool.

Common causes

1Multiple large datasets are refreshing simultaneously, exhausting available memory
2A single dataset with a very high cardinality causes memory pressure for all other operations
3Report queries from many concurrent users are competing with background refresh operations
4The capacity SKU is undersized for the number of workspaces and datasets it hosts
5Auto-scale is not enabled and capacity spikes cannot be handled by the base SKU

How to fix it

1Open the Premium Capacity Metrics app to identify which workspaces and datasets consume the most CPU and memory.
2Enable capacity auto-scale (if available in your region) to handle peak loads.
3Stagger refresh schedules to avoid simultaneous peaks — spread refreshes across different hours.
4Move large or frequently-refreshed datasets to a dedicated capacity to isolate their resource usage.
5Enable incremental refresh for the largest datasets to reduce per-refresh memory requirements.

Frequently asked questions

Does this error mean I need to upgrade my capacity?

Not necessarily — capacity errors can be caused by query parallelism, large models, or concurrent refreshes. Optimize heavy datasets and schedule refreshes off-peak before upgrading.

Does this error affect all workspaces on the capacity?

Yes — Premium and Fabric capacities are shared. Heavy usage in one workspace can cause capacity errors in others on the same SKU.

Can I retry a failed refresh without waiting for the next schedule?

Yes — go to the dataset in Power BI Service and click 'Refresh now'. If the capacity issue was transient, the retry usually succeeds.

Does Power BI automatically retry capacity-related failures?

Power BI does not automatically retry failed refreshes. You need to trigger a manual refresh or wait for the next scheduled slot.

Source · learn.microsoft.com/en-us/power-bi/enterprise/service-premium-large-models