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A low-code app can feel instant with sample data and still become slow—or return incomplete results—when a customer’s real data arrives. The key question is not simply how many rows the platform can store. It is whether the app asks the data source to filter and process records, retrieves only the rows and columns it needs, and uses paging appropriate to the table and workload.
Why an app that worked with sample data can struggle with customer data
A small sample can hide inefficient queries. With more records, an app may retrieve too much data, process some of it locally, or use a paging pattern that becomes costly as results grow. These are different problems: storage capacity alone does not establish that a screen can find, load, or display the right records efficiently.
There is no universal row-count threshold at which every low-code app becomes slow. The outcome depends on the app’s formulas, connector, data source, table shape, and user interactions. Microsoft’s Power Apps guidance focuses on whether a Power Fx query can be translated into a query the connected source supports. When it can, the source does the filtering or processing; when part of a query cannot be delegated, Power Apps retrieves a limited set and performs that work locally. Microsoft Learn: Understand delegation in a canvas app.
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In Power Apps, delegation affects both speed and correctness
Delegation means sending supported query work to the data source rather than asking the app to retrieve a subset and process it on the device. Whether a formula delegates depends on the function, data source, and connector—not just on how the formula looks.
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For nondelegable queries, Power Apps uses a local data row limit that is 500 records by default and can be raised to 2,000, according to Microsoft’s page updated January 13, 2026. This is a limit on the records used for local processing, not a maximum size for the underlying table.
The distinction matters for correctness as well as responsiveness. If a nondelegable filter searches only the retrieved set, a matching record outside that set may not appear even though it exists in the full table. Raising the limit can include more records, but Microsoft warns that larger result sets can affect performance, particularly with wide tables, and recommends delegating as much as possible. Increasing the limit is therefore not a substitute for making the query delegable. Microsoft Learn: Understand delegation in a canvas app.
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Retrieve fewer rows and columns
Even when a query is delegated, an app can do unnecessary work if it requests a broad result or carries more data than the screen needs. Microsoft recommends using supported source-side filtering and limiting the data retrieved. A gallery or table control bound directly to a remote data source can page results in small increments—for example, 100 records—rather than requiring the app to load the entire dataset at once. That figure is an example in Microsoft’s guidance, not a universal batch size or performance guarantee. Microsoft Learn: Small data payloads – limit the amount of data you get.
Design each screen around the user’s actual task: filter at the source where supported, return the fields the screen uses, and avoid loading a broad table merely to narrow it after retrieval. This keeps the data payload tied to the interaction rather than to the full size of the customer’s dataset.
Dataverse paging is separate from the canvas app local row limit
Dataverse query paging describes how query results are retrieved in pages; it is not the same setting as Power Apps’ local row limit for nondelegable processing. For QueryExpression, Microsoft documents these default and maximum page sizes:
| Dataverse table type | Default page size | Maximum page size |
|---|---|---|
| Standard | 5,000 rows | 5,000 rows |
| Elastic | 500 rows | 500 rows |
Microsoft recommends paging cookies for all dataset sizes. Simple paging is intended for small datasets, has a total ceiling of 50,000 records, and loses performance as the result set grows. These figures describe Dataverse query paging behavior; they are not maximum stored-table capacities. Microsoft Learn: Page results using QueryExpression.
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Microsoft describes elastic tables as an option for workloads requiring large volumes and scalable throughput. That does not make an elastic table an automatic fix for a slow app: Dataverse throttling limits still apply, and the query and interaction pattern remain important. Consider the table type alongside the way users query the data, the required paging behavior, and the actual workload. Microsoft Learn: Create and edit elastic tables.
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How to diagnose a customer-data slowdown
- Find the affected interaction. Identify the screen, search, filter, or refresh that slows down or misses a record, and reproduce it against the customer’s data.
- Check whether the query delegates. Inspect the Power Fx formula and the connected source’s supported operations. A nondelegable part can cause the app to process only a limited local set.
- Verify result correctness. Test a known matching record that falls outside the local row limit. A missing result may be a partial-query problem rather than evidence that the record is absent.
- Reduce the payload. Filter at the source where supported and request only data needed by the current screen. Use paging rather than loading a broad dataset into the app.
- Review Dataverse paging when applicable. Confirm the table type and use the documented paging mechanism for the query. Do not confuse a Dataverse page size with the Power Apps local row limit.
- Measure the actual app and workload. Observe the affected query and screen with representative customer data. Documentation describes platform behavior, but it cannot establish how a particular app performs.
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