Out of memory issues on n8n Cloud after recent update

Questions
1

Hello n8n Community,

I am facing a serious out-of-memory issue on my n8n Cloud instance after the recent n8n update.

My workflows were working completely fine until 18 May and had been running reliably for the past 6 months. I did not make any workflow, node, credential, configuration, or environment changes during the past week.

After the recent n8n version update, multiple production workflows started failing during execution. When these workflows run, the instance becomes unstable or unresponsive, automations stop running, and the instance eventually runs out of memory.

I have already tried deleting saved executions, reducing execution history, and removing Wait nodes to reduce memory usage, but the issue still continues. Even after deleting executions, when the workflows run again, the instance still goes out of memory or crashes.

This started only after the update, so I want to understand whether there was any recent change in n8n Cloud related to memory handling, execution behavior, child executions, workflow concurrency, or node behavior.

I would appreciate guidance from the n8n team or community on how to debug this and identify what is causing the memory spike.

I would also like to know if anyone else is facing the same issue after the recent update, or if this is only happening on my instance.

What is the error message, if any?

The main issue is that the instance runs out of memory during workflow execution.

In the execution list, the workflows show as Error.

Some recent failed execution IDs are:

Execution ID: 1074560
Status: Error
Started: May 21, 12:53:34
Runtime: 11.722s

Execution ID: 1074559
Status: Error
Started: May 21, 12:53:31
Runtime: 5.182s

Workflow:

These are production workflows, so I cannot share the full workflow JSON publicly because they contain business logic, credentials, and integrations.

output returned by the last node

The workflows fail before completing successfully. The execution status shows Error, and the main issue is out-of-memory behavior at the instance level.

In some cases, the executions fail very quickly, for example within milliseconds. In other cases, they run for several seconds before failing.

The instance also becomes unstable or unresponsive when the workflows run.

Information on your n8n setup

n8n version:
n8n Cloud latest updated version

Database:
n8n Cloud managed database.

n8n EXECUTIONS_PROCESS setting:
n8n Cloud managed / not directly configured by me.

Running n8n via:
n8n Cloud.

Operating system:
n8n Cloud managed / not applicable.

Additional notes

I recently paid for the next yearly plan and I am on the Pro 1 Plan. The workflows were stable before the recent update. This issue started after the update, without any changes from my side.

I would like to know:

  1. How can I identify which workflow or node is causing the memory spike?
  2. Was there any recent n8n update that changed memory usage, execution handling, workflow concurrency, or node behavior?
  3. Is there any workaround, patch, rollback option, or recommended setting to stabilize the instance?
2

welcome to the n8n community @Asher_TMT
1- no
2- i didn’t find any regressions
3- To stabilize while you investigate, try running fewer things at the same time, reduce the size of data being processed, avoid large files within the workflow, and split heavy flows into smaller parts. If a particular process is very heavy, it might be worth moving that step to an external service and leaving n8n just orchestrating.

3

and do not use Loop nodes. Especially with big amount of items. They are very heavy for processing.

4

Since the last update, we have reduced a lot of things, but the concerning part is that the workflows had been working fine for the past 6 months. I previously handled much heavier loads, and N8N never crashed. Now, even though I am only processing around 60% of that load, it is still crashing.

5

@Asher_TMT
Do the workflows that started failing after the update have binary processing/AI nodes/loops or high concurrency? And does the behavior improve if you temporarily reduce parallel executions?

6

I’m seeing this pattern this week on multiple n8n Cloud instances. This isn’t a workflow configuration problem, but looks like a memory regression in the latest update.

Two diagnostic questions:

  • Are your workflows using Sub-Workflow nodes? There’s currently a known memory leak pattern there.

  • Does it always crash on the same node or randomly?

As an immediate workaround, reduce parallel executions to 1 in the workflow settings — this should stabilize the instance while you debug.

If you tell me which node types you’re using (no credentials needed), I can help you more specifically.

7

Hi @Kemal_Automation, good morning!
According to the community guide, we cannot repeat responses that already exist in the thread.
Could you please review your response to confirm if there are any redundancies with the recorded responses?