Why Power Apps Works in Testing but Slows Down in Production

This is one of the most confusing moments in a Power Apps project.

The app worked perfectly during development.
Testing went smoothly.
Users were happy during demos.

Then it went live.

Within weeks:

• Screens started loading slower
• Searches felt inconsistent
• Some users complained more than others
• The phrase “It was faster before” started appearing

Nothing obvious changed — yet performance clearly did.

After reviewing many Power Apps apps during post-go-live performance reviews, one pattern keeps repeating:
Power Apps doesn’t slow down randomly in production.
It slows down when real-world conditions finally show up.

A Very Common Real-World Pattern

This sequence appears again and again:

• App is built using sample or limited test data
• Few users test it
• Data volume is small
• Performance feels instant

Then production brings:

• Full historical data
• Real user behaviour
• Concurrent usage
• Edge cases

The app hasn’t changed — the environment has.

1. Test Data Rarely Represents Real Data

One of the biggest differences between testing and production is data volume and shape.

In testing:

• Tables are small
• Lookups are fast
• Filters stay within limits

In production:

• Lists and tables grow quickly
• Historical records accumulate
• Delegation limits are crossed

What felt instant with 500 records behaves very differently with 50,000.

What teams realise later:
Performance assumptions based on test data are almost always optimistic.

2. Delegation Issues Only Surface at Scale

Delegation warnings are often present during development — but ignored.

Why?

• Results look correct
• Performance feels fine
• No visible errors appear

In production:

• Data exceeds delegation limits
• Filters return partial results
• Power Apps processes more data locally
• Performance degrades

The app didn’t become slow overnight — it crossed a delegation threshold.

3. Real Users Use Apps Differently Than Testers

Testers tend to:

• Follow expected paths
• Use common filters
• Avoid extreme scenarios

Real users:

• Search broadly
• Apply multiple filters
• Navigate quickly between screens
• Use the app in unpredictable ways

This triggers:

• More queries
• More recalculations
• More data processing

The app wasn’t designed for that behaviour.

4. Concurrency Changes Everything

In testing:

• One or two users at a time

In production:

• Many users accessing the same data
• Multiple refreshes
• Simultaneous searches

Even well-designed apps can feel slower if concurrency wasn’t considered.

This is especially noticeable when:

• Data sources are shared
• Queries are not delegated
• Logic runs on every screen load

5. Logic That Felt Fine in Testing Adds Up in Production

Many apps include:

• Heavy logic in OnVisible
• Repeated collection rebuilds
• Nested formulas that recalculate frequently

In testing, this overhead is invisible.

In production, repeated small costs turn into noticeable delays.

What teams learn later:
Power Apps evaluates logic more often than expected.

6. Environment Differences Matter

Production environments are often:

• More restricted
• More secure
• More governed

This can affect:

• Data access
• Connector behaviour
• Response times

The app logic didn’t change — but the execution context did.

Why This Catches Teams Off Guard

Most teams test for:

• Functional correctness

Very few test for:

• Data growth
• Realistic usage
• Long-term behaviour

As a result, performance issues feel like a surprise — even though they were predictable.

How Teams Successfully Fix Production Slowness

Across real projects, the most effective fixes usually include:

• Reducing how much data loads initially
• Fixing delegation issues early
• Simplifying screen-level logic
• Moving logic closer to the data source
• Designing with scale in mind

These fixes rarely involve rebuilding the entire app — but they do require revisiting early design choices.

This separation between Power Apps and the data layer is where many real solutions either stabilise — or continue to struggle. For readers interested in understanding how Power Apps, data sources, and automation should work together in real business environments, this Microsoft Power Apps approach is explained here: Microsoft Power Apps & Power Automate

Final Thought

When a Power App slows down in production, it’s not because Power Apps “failed”.

It’s because:

• Real data arrived
• Real users arrived
• Real usage patterns emerged

Power Apps performs best when apps are designed not just to work, but to scale.

Testing proves correctness.
Production reveals design quality.

Learning Power Apps the Right Way

For those looking to understand how Power Apps behaves under real-world conditions — including data growth, delegation, performance, and automation — the Microsoft Power Apps Course by ExcelGoodies focuses on practical scenarios drawn from live projects, not idealised demos.

Check the Upcoming batch details

Editor’s Note

This article reflects recurring post-deployment performance discussions observed across live Power Apps implementations, where apps behaved well during testing but struggled under real-world usage. The scenarios described highlight common patterns rather than isolated issues.

Insights compiled with inputs from the ExcelGoodies Trainers & Power Users Community.
 

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