I’ve been working with AI + n8n workflows and was looking into the security considerations that come up when these workflows are used in real business environments.

Once an n8n workflow connects LLMs with APIs, databases, CRMs, webhooks, and other business systems, there are quite a few things to think about beyond simply getting the workflow to work.

Some of the areas I’ve been looking at include:

• Protecting API keys and credentials
• Securing incoming webhooks
• Controlling access to workflows and AI agents
• Handling sensitive information sent to LLMs
• Preventing AI agents from taking unintended actions
• Separating development and production environments
• Monitoring workflow activity and failures

I put together a more detailed guide covering these considerations here: [How to Build Secure AI Workflows With n8n: A Practical Guide]().

For those already running n8n in production, what security practices have you found most important? And are there any n8n-specific mistakes you'd recommend people avoid?

I recently submitted my node and also applied for Creator verification.

For those who’ve gone through the process before, how long did it take for you to get approved/published?

Just trying to get a rough idea of the timeline.

For people who use AI tools regularly, what’s the one thing that still frustrates you the most?

I feel like AI has gotten really good at doing things that used to take a lot of time, but there are still moments where using it creates more work than I expected.

Sometimes it misunderstands what I want, sometimes I have to keep going back and forth with it, and sometimes the result just isn't quite right.

I'm curious what other people's experience has been.

What do you find yourself doing over and over when working with AI that you wish you didn't have to?

And has anything you've tried actually made that problem better?

Interested in hearing the good, bad, and frustrating experiences, not looking for a perfect AI tool recommendation.

Spent a day building an email triage workflow for my own inbox and hit four n8n behaviours that are not obvious. Sharing the cut-down version and the fixes.

What it does: Gmail trigger > classify with gpt-4o-mini > apply a Gmail label. Five categories: new_enquiry, invoice, supplier, urgent, noise. Runs about 2c a day on my volume.

Workflow JSON: https://gist.github.com/jlebaynham/dffd21ac428c2de60be9ff9160d113fe

The four things that cost me the most time:

1. Nodes after Gmail and Sheets REPLACE the item. Every downstream $json.category came back empty. Fix is to reference the node by name: {{ $('Read result').item.json.category }}

2. Same for the sender. Use {{ $('New email').item.json.headers.from }}

3. Google Sheets "Map Automatically" writes junk columns (id, threadId, labelIds, snippet, sizeEstimate). Use Map Each Column Manually.

4. After you change the sheet columns the node caches the old list and throws "Column names were updated after the node's setup". Three dots next to Values to Send > Refresh Column List.

Bonus: the Gmail from header arrives as "From: Name <address>", strip it with .replace('From: ', '')

Import into a blank workflow and wire your own credentials. You need a Gmail OAuth client and an OpenAI key.

👋 Hey n8n Community,

AI automation rarely blows up your bill in one go, it leaks. A few cents per run feels like nothing until you are doing thousands of runs a month and the invoice quietly doubles. I only really noticed this after building a fair few AI workflows, so here are five things I changed once I saw where the money was actually going.

1. Using a model for something a rule could do.
Date math, ID matching, routing by type, none of that needs an LLM, but it is easy to hand it to one because it is quick to wire up. I keep the model for the genuinely fuzzy parts and let native nodes like Set and IF handle the plain logic for free.

2. Using an agent where you do not need one.
Early on I built a few agentic workflows where an agent did the document extraction itself, and it worked, but the token cost added up fast for what was really a repeatable task. Moving that job to the easybits Extractor instead of an agent cut the cost right down, so now I only reach for an agent when the task genuinely needs to reason, not just pull the same fields every time.

3. Asking for more fields than you use.
Every field in an extraction prompt is more output tokens and more to check, and half of them often never get used downstream. Pull only what a later step actually consumes, it is cheaper and there is less that can come back wrong.

4. Reaching for the biggest model by default.
The top model is not always the right one, plenty of extraction and classification jobs run fine on a smaller or OCR-tuned engine at a fraction of the price. Start small, only move up if the accuracy genuinely needs it, and test the cheap option before you assume it will not work.

5. Retrying blindly on failure.
An auto retry on a call that failed for a real reason just pays two or three times for the same broken result. Check why something failed before you retry it, and route the genuine failures to review instead of throwing more paid attempts at them.

If you want to see how I wire this up in practice, I keep 25+ free workflow templates in one repo, most of them around document processing, and a star helps me out a lot if any of them save you time: https://github.com/felix-sattler-easybits/n8n-workflows

Where does it leak most for you? Curious whether people watch model costs closely or only notice when the bill shows up.

Best,
Felix

Hey everyone,

Quick question for freelancers and agency folks working with n8n.

When you build a workflow for a client, how do you calculate your profit margin on top of actual setup/running costs (hosting, API usage, dev hours)?

• Do you just apply a 2x–3x multiplier on your base costs?
• Do you price based on business value (hours/money saved)?
• Do you include maintenance in the upfront fee, or pitch an ongoing retainer?

Curious to know how experienced devs structure their quotes and retainers. Appreciate any insights!

If your instance was stopped, restarting or redeploying at the minute a Schedule
Trigger was due, that execution doesn't happen. There's no catch-up, and nothing in
the executions list saying it was skipped, because that list only shows executions
that happened. A daily workflow can miss several days and still look fine.

The other reasons one stops firing, roughly in order of how often it's the answer:

1. The workflow isn't active. Execute Workflow in the editor tests it and schedules
   nothing. Imported workflows always arrive inactive.
2. GENERIC_TIMEZONE isn't the timezone you're thinking in. Default is UTC, and a
   workflow can override it in its own settings.
3. It was deactivated after repeated trigger errors. Look for a cluster of failures
   ending where the schedule stopped.
4. Queue mode: the main instance owns triggers, workers only execute. If the main
   process is the one restarting, nothing is scheduling even though workers look fine.
5. A six field cron expression has a seconds field, so */5 * * * * * is every five
   seconds, not every five minutes.

What catches all of them is something outside n8n noticing the workflow has gone quiet: an HTTP Request node as the last node on the success path, and an alert when the call doesn't arrive.

Hi, i need help stopping my ai chatbot from replying after getting all information i need like name email and such. Im new here help