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A sudden Power Query slowdown can come from changed query folding, repeated evaluations, a slower data source, or pressure on your computer, gateway, network, or Power BI service capacity. The first step is to identify which refresh stage slowed down; then compare the source requests and resource use to find the cause. A spinner alone does not tell you where the time went.
First find where the slowdown happens
Separate a slow Power Query Editor preview, a slow Desktop load or Apply operation, and a slow refresh of a published semantic model in the Power BI service. Record the affected query or table, the old and new approximate durations, when the change began, whether the source or credentials changed, and whether the delay is consistent.
For a published model, open its refresh history in the Power BI service. It records status, start time, duration, and error details. If only Desktop is slow, inspect local CPU and memory use and evaluation settings. If only the service is slow, prioritize the gateway, network, source workload, and service resource limits.
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Check whether query folding changed
Query folding is Power Query’s ability to translate supported transformations into operations performed by the data source. If a step stops folding, more work may move to the local mashup engine, and more rows may have to travel from the source. Microsoft recommends folding transformations where possible for relational sources when a single source-side SELECT can represent the query. See Microsoft’s query-folding overview.
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Use Query Diagnostics to inspect the most specific source query Power Query emitted and determine which transformations ran at the source and which ran locally. Folding indicators can also show whether steps fold in Power Query Online; availability depends on the host. Microsoft describes diagnostics as a way to understand which operations Power Query pushes down to a back-end source in Understand Folding by using Query Diagnostics.
- Check whether filters or column selection that used to reach the source are now applied locally.
- Look for a new or changed transformation the connector cannot translate.
- For incremental refresh, verify that the
RangeStartandRangeEndparameter types match the source date column, and confirm the range filter appears in the query sent to the source. See Configure incremental refresh and real-time data for Power BI semantic models.
One Microsoft example demonstrates why folding is worth checking, but its timings are not a general prediction: in a 2025 Power BI dataflow test against dedicated Azure Synapse Analytics at DW2000c, refresh took 361 seconds with no folding, 184 seconds with partial folding, and 31 seconds with full folding. Those results apply to that test setup, not every connector, query, or workload. Details are in Query folding examples.
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Find out whether Power Query is issuing repeated requests
More than one evaluation is not automatically a bug or a live network request. A connector may fetch metadata or paginate results; separate queries may reference the same upstream query; and folding checks, privacy analysis, background previews, or column profiling can trigger additional evaluations. Query Diagnostics records evaluation activity, but caching means some entries may not correspond to a fresh request to the source. Compare the emitted source queries and isolate likely causes one at a time. Microsoft’s explanation of repeated runs is at Why does my query run multiple times?.
Do not assume a referenced query is computed once and shared. Separate queries that reference it can execute the upstream query separately. Adding Table.Buffer to the upstream query does not make that buffer reusable across independent executions; each execution may buffer again. See Microsoft’s guidance on multiple query evaluations.
Review privacy settings without disabling safeguards
When a query combines sources, privacy levels can constrain how data moves between them. That can add buffering and affect performance, but ignoring privacy levels can expose sensitive or confidential data to an unintended source. Review the sources’ privacy classifications and the way the queries combine them; do not turn off safeguards as a blanket speed fix. Microsoft’s explanation is in Privacy levels in Power Query.
For service-only delays, check the gateway and workload
If Desktop remains fast but a published model refresh slows, check whether the gateway is online and configured appropriately, credentials are still valid, and the source or gateway is under load. Gateway placement and network distance from both the source and the Power BI tenant can add latency. Larger volumes, source timeouts, concurrent partition refreshes, and service memory or capacity limits can also lengthen or interrupt a refresh. See Data refresh in Power BI.
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- Compare rows or bytes processed when those measurements are available, and check source-side duration against total refresh duration.
- Remove unnecessary columns, rows, or transformations when evidence shows they create avoidable work.
- Check whether refresh parallelism or concurrent workloads are overloading the source or capacity.
- Do not increase timeouts first if the query is transferring excess data or has lost a source-side filter; address the work causing the delay before extending the wait.
For Desktop-only delays, inspect local evaluation settings
Power BI Desktop’s query evaluation settings control local evaluation parallelism and resource use. More parallel work can affect machine load, so compare CPU and memory while the slow refresh runs. These settings affect evaluation on the local computer, not Power BI service refreshes. See Configure query evaluation settings in Power BI Desktop.
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After making one evidence-based change, compare the same query and refresh path before and after. Use elapsed time alongside the source query text and source-side duration, rows and columns fetched, local evaluation time, repeated request count, and machine, gateway, or service resource pressure. For service refreshes, include the refresh-history status, duration, and error details. This helps distinguish a real reduction in work from a change that merely shifts where the delay occurs.
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