How to Debug a 429 Rate Limit Error in Make.com

I ran into a frustrating issue with a Make.com workflow recently.

The scenario was working through most of its steps without any problems. The webhook received the data, Notion created a database item, Telegram sent a message, and the JSON module completed successfully.

Then the HTTP module failed.

The error was:

429 Too Many Requests

The API response also included:

You have exceeded your current request limit.

I was using Alibaba Cloud's Qwen-Turbo model through its API. The first instinct might be to change the API configuration or rebuild the scenario, but neither should be the first move.

Here's how I approached the problem.

1. Find the module that actually failed

When a Make scenario fails, don't assume the entire workflow is broken.

Open the scenario's execution history and inspect the latest failed run.

Look at each module's execution status. In my case, the webhook, Notion, Telegram, and JSON modules had all completed successfully. The HTTP module was the first one to fail.

That immediately narrowed the investigation.

There's no reason to rebuild working modules when the failure is isolated to one API request.

2. Read the actual API error

The HTTP module showed a rate-limit error, so I checked the API provider's request logs.

The response included:

• HTTP status: 429
• Error code: limit_requests
• Error message: You have exceeded your current request limit.
• Model: qwen-turbo

This was much more useful than simply seeing a red error icon in Make.

A 429 response generally indicates that a service is refusing a request because a usage or request limit has been reached. The exact limit can vary by provider, model, account, and service configuration.

The important thing is to read the provider's error details rather than guessing.

3. Stop replaying the failed run

It's tempting to click Replay every time a scenario fails.

But if the API is still rate-limiting requests, repeatedly replaying the same run may only produce more failures.

I stopped retrying while investigating the issue.

This also helps avoid unnecessary API calls and makes it easier to tell whether new requests are still arriving from another source.

4. Check for other sources of API traffic

A rate limit isn't always caused by the scenario you're looking at.

Other Make scenarios, local scripts, or applications may be using the same model or account.

I would check:

• Whether other scenarios call the same API.
• Whether a local application is making requests in the background.
• Whether multiple workflows can run at the same time.
• Whether the provider's logs show new requests after the scenario is paused.

Don't assume that every request in the provider's logs came from the scenario you're debugging.

5. Add retries carefully

Retries can help with temporary rate limits, but an immediate retry loop can make the problem worse.

A safer approach is to use delayed retries with increasing wait times, for example:

• First retry: wait 10 seconds.
• Second retry: wait 30 seconds.
• Third retry: wait 60 seconds.

These are starting points, not universal settings. The appropriate delay depends on the provider's limits and any retry guidance in its response headers or documentation.

For persistent quota limits, waiting and retrying won't solve the underlying problem. You need to inspect the account or model quota instead.

Also make sure failed runs don't trigger an uncontrolled chain of duplicate requests.

6. Change only what the evidence supports

After identifying the failing module, I avoid changing the model, API endpoint, request body, and credentials all at once.

Changing several things simultaneously makes it harder to identify the actual cause.

Instead, I check the specific limit reported by the provider, make one targeted adjustment, and test again.

If the API request succeeds, I then verify that the downstream modules receive the expected response.

A simple debugging checklist

When an API request fails in Make.com, I now work through this order:

1. Open the failed execution.
2. Identify the first module that failed.
3. Read the complete error response.
4. Check the API provider's request logs.
5. Pause repeated requests while investigating.
6. Check for concurrent traffic and usage limits.
7. Make one targeted change.
8. Run a controlled test and verify the downstream steps.

The biggest lesson for me is that a failed automation doesn't always need more complexity.

Sometimes the fastest fix is to stop changing things, find the first failing step, and read what the service is actually telling you.

How do you usually handle API rate limits in your automation workflows? Do you use delayed retries, a queue, or another approach?