In my opinion it is always best to use the right tool for the job. In the case of n8n, it is great for automating and orchestrating simple tasks. Now on top of this the architecture of your solution is always the most important thing to get right. You can still build a slow solution using the fastest of languages and tools. So, yes it is important to evaluate your use case to see if n8n is suitable. You can still make use of it but off loading certain heavy lifting tasks to tools which is built to handle for example large datasets.
For large datasets, id look at batch processing or event driven arrchitectures depending on what you’re trrying to do. You’ll probably need to self host an instance of n8n and add enough memory to handle the datasets you need to process.
Team Colab - the enterprisse versions do have git support but on smaller teams we used a single login and communication to get around this issue. We also have a workflow which would push regular workflow changes to git. n8n workflows are just simple text json files so you can still get a git diff if you rreally needed to know what changed.
Complex workflows - As a rule I would always break down my workflows into smaller manageable workflows which you can call from a main orchestration workflow.
High Traffic apps, again you’ll need to self host and set it up to scale. This is generally done by running it in queue mode as well as running multiple instances and load balancing them. This is no different from setting up infrratructure for any other application running java or whatever.
AI Agents - I have not come across this issue, but this can be a common issue with any ai agent platform so make sure your design is done right. Hallucinations is a common trait in LLMs so tweaking system prompts could reduce this.
Just my opinions. Happy to further discuss any specific requirement you have to see if it could be built with n8n.
It also helps to instruct the agent in the system prompt to avoid hallucinating. It’s also worth playing with the temperature and top-p settings if you need more consistent results over creative variation.
I have literally posted this to my Linkedin a couple of days ago:
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Yesterday I built a script.
It processes 500 sales call MP3s, transcribes them, diarizes speakers (with correct “customer”& “operator” mapping resolution), and extracts insights and objections. Instead of spending 20 hours in n8n, I spent just 3 hours in Claude Code and got a far more refined result. Two years ago, building something like this would have taken me a full week of development.
It is becoming crystal clear that Codex and Claude Code have advanced to the point where using complex no-code platforms makes little sense. Why spend weeks inside a vendor-locked system when that time could go toward foundational work using better technology? While n8n is not going anywhere and it is still great for simple scenarios, my bet is that the real focus for SMBs might soon shift away from mastering no code and low code platforms and toward creating simpler apps in ‘real’ programming languages AND bringing order to this internal zoo: running countless Python/Node.js micro-apps while governing user and data access internally.
If generating your flows is more your thing, then look at the below. My opinion on using claude code and gpt blindly for non technical people is that it produces a black box and you dont know what’s inside unless you’re technical enough to code review to make sure things are built correctly and securely.
Did you use FlowMod ? I have tried a couple of similar n8n chrome extensions with big words about AI on their landing pages, and they simply did not work adequately on real-world n8n scenarios - I think n8n is just not a good fit for serious API/MCP-powered workflow creation
I have not yet, joined the waiting list to get access to try it out, but it looks promising compared to the others. Yeah there are a number tools and workflow agents built to try and generate n8n workflows. You still have to tweak and fix some of them. I still prefer to build my workflows by hand as I think its quicker than to prompt a 100 fixes for gen ai ones
I agree it’s not a great fit for everything. For simple workflows which are non-critical to your business like syncing data between systems and simple agents it works great, however I wont build a core banking system on it for example, so right tool for the job depending on your requirements I guess. Definitely a great tool for quick POCs and stringing together rest calls vs using postman for example.
I was just looking at another post now where someone asked about text to workflow. I forgot that that is a feature coming, but isnt that only available for cloud users and not community instances? Im sure the native n8n version will be more accurate as the team can probably better train it.
Surup from Neuronics AI Agency just shared when N8N hits its limits for AI automation:
by +50% when I disconnected the memory from ai agent.
