Power Automate allows users to create loops using “Apply to Each”, “Do Until”, and “For Each” actions. However, if a loop exceeds the platform’s iteration limit, the flow will fail with a “Loop Limit Exceeded” error.
This guide explains:
Common causes of excessive loop iterations
How to diagnose and fix the issue
Best practices to optimize loops and prevent failures
Step 1: Understanding Loop Limits in Power Automate
| Loop Type | Maximum Iterations Allowed |
|---|---|
| Apply to Each | 100,000 iterations per flow run |
| Do Until | 5,000 iterations per flow run |
| For Each | 100,000 iterations per flow run |
Power Automate imposes these limits to prevent infinite loops and excessive resource consumption.
Step 2: Diagnosing the “Loop Limit Exceeded” Error
2.1. Check Flow Run History
- Open Power Automate (https://make.powerautomate.com).
- Go to My Flows → Select the failing flow.
- Click Run History and open a failed run.
- Locate the loop action (e.g., “Apply to Each”, “Do Until”).
- Check how many times the loop ran before failure.
2.2. Identify the Root Cause
The loop might be running excessively due to:
✅ Too many records – The loop is processing an unexpectedly large dataset.
✅ Incorrect exit conditions – The “Do Until” loop never meets its exit criteria.
✅ Inefficient filtering – The flow iterates over unnecessary items.
Step 3: Fixing the “Loop Limit Exceeded” Error
3.1. Reduce the Number of Items Processed
Problem: The loop processes too many items at once.
Solution:
- Use a “Filter Array” action before the loop to remove unnecessary items.
- Use pagination for large datasets.
Example: Filtering a SharePoint list before looping:
Filter: Status equals "Pending"
Only loop through pending items instead of all records
3.2. Optimize “Apply to Each” for Large Datasets
Problem: Processing too many records from SharePoint, Dataverse, or Excel.
Solution:
- Use “Top Count” to limit results in a Get Items action.
- Consider using Parallel Processing (see Step 3.5).
3.3. Fix “Do Until” Exit Condition Issues
Problem: The “Do Until” loop never meets its condition, causing an infinite loop.
Solution:
- Add a counter variable to exit after a set number of iterations.
Example: Preventing an infinite “Do Until” loop
Set counter = 0
Do Until (Status = "Complete" OR counter > 100)
Increment counter by 1
This ensures the loop does not exceed 100 iterations.
3.4. Use Data Chunking Instead of Large Loops
Problem: The flow processes huge datasets in a single loop.
Solution:
- Instead of processing 100,000 records in one run, split the data into smaller chunks.
- Use “Skip” and “Top” in queries to process records in batches.
Example: Processing 500 records at a time in SharePoint
Get first 500 records → Process them → Skip 500 → Get next 500 → Repeat
This prevents hitting the 100,000 iteration limit.
3.5. Enable Parallel Processing (For “Apply to Each”)
Problem: The flow takes too long because it processes items one by one.
Solution:
- Enable concurrent processing in the “Apply to Each” action:
- Click the three dots (
...) on “Apply to Each”. - Select Settings.
- Enable Concurrency Control and set Degree of Parallelism (e.g., 50).
- Click the three dots (
🔹 This allows multiple iterations to run at the same time, reducing execution time.
3.6. Use Expression-Based Filtering to Reduce Loops
Problem: The loop iterates through all items, even when only a few are relevant.
Solution:
- Use filter expressions before entering the loop.
Example: Query only “Pending” items from SharePoint instead of all records
OData Filter Query: Status eq 'Pending'
This prevents looping over unnecessary data.
3.7. Switch to Alternative Methods (Power Automate Limits Workarounds)
Problem: The loop limit cannot be reduced, but needs to process a large dataset.
Solution:
- Use Power Automate Desktop for large automation tasks.
- Use Azure Logic Apps (no hard iteration limits).
- Break the flow into multiple sub-flows to process data in stages.
Step 4: Preventing Future “Loop Limit Exceeded” Errors
✅ 4.1. Always Filter Data Before Processing
- Use “Filter Query” instead of retrieving all records.
- Reduce the number of items sent to “Apply to Each”.
✅ 4.2. Monitor Flow Run Performance
- Regularly check Flow Run History for long-running loops.
- Set alerts for excessive execution times.
✅ 4.3. Use Batch Processing for Large Data Sets
- Instead of looping through 100,000 records in one run, split data into batches of 1,000.
✅ 4.4. Add Exit Conditions to Loops
- For “Do Until” loops, always include a backup counter to prevent infinite loops.
Step 5: Setting Up Alerts for Loop Issues
To detect excessive loops early, set up a notification:
- Add a “Condition” action before the loop.
- If the loop exceeds 5,000 iterations, send an email notification to the admin.
- Example: If counter > 5000 → Send alert email “Loop limit exceeded!”