n8n Is Not Enough Anymore", "author": "Chase H", "source": "PUBLICATION: Chase H AI", "body": "The argument being made here is that the automation landscape has fundamentally shifted — and thousands of non-technical users need to hear this.
Six months ago, if you wanted to create a complex AI automation or agent as a non-technical user, n8n was essentially the only viable option. The alternatives were either writing actual code or using early versions of agentic coding tools like Claude Code — which still required significant technical knowledge to get the most out of them.
That dynamic has completely changed. Tools like Claude Code, Codeex, and Anti-Gravity have become so advanced and user-friendly that you no longer need to know how to code to achieve complex results. The ceiling for what these tools can accomplish now exceeds n8n's capabilities — and the implications for anyone building AI systems are significant.
Where n8n Struggles
### Scaling Limitations
The first challenge involves scaling your project itself. Once a workflow reaches 50 or 60 nodes, it becomes brittle. The more deeply nested logic, loops, and error handling you add, the more you encounter fundamental infrastructure limitations.
Those same solutions inside n8n could easily be handled with just a few hundred lines of code in Claude Code — code that's far easier to test and scale.
The second scaling issue involves throughput: hundreds of users running in parallel, thousands of API calls per hour, large datasets. These scenarios expose n8n's struggles significantly. When you find yourself implementing hacky workarounds inside n8n to solve your problem, that's the moment to ask whether Claude Code and proper code would solve it more elegantly.
### Speed to Deployment on Complex Projects
This might sound surprising since speed is supposedly n8n's strength — but it's only true for simple or medium-difficulty projects. For complex issues, navigating Claude Code or Codeex and prompting your way to a solution is infinitely faster than tying together dozens of nodes in n8n.
The output you get with real code is also easier to iterate upon, test, and extend later. This applies whether you're on the cloud version or self-hosted version using their visual workflow builder.
### Commercial Licensing Restrictions
This issue doesn't get discussed enough: n8n's fair use policy is confusing. Many creators incorrectly reference it as open source, which simply isn't true.
The practical problem is significant: if you build something inside n8n and want to sell it or use it as a SaaS project, you're restricted in certain scenarios. Specifically, you cannot require users to provide credentials — imagine building an AI assistant that needs your email or Gmail credentials to function. You can't do this with n8n.
That severely limits what you can build commercially. Compare this to Claude Code: you own the code, you can sell it however you want, you can package it however you want using open source components. For anyone trying to build something profitable, this is a massive constraint.
Where n8n Still Wins
### Speed on Simple Projects
For simple or medium-sized projects, n8n remains king. You can assemble a quick MVP with a linear idea in 5-10 minutes using a visual interface that's easy to explain to non-technical teams and troubleshoot. Trying to do the same in Claude Code involves additional overhead that may not make sense for early stages.
### Accessibility for Non-Technical Teams
This can't be dismissed: n8n's meteoric rise came from allowing non-technical people to actually get their hands dirty building things completely outside their wheelhouse. If you're dealing with a client or team who isn't technical but needs to edit and troubleshoot the project, n8n is brilliant.
The alternative — giving them a Python app in a GitHub repo to edit as needed — is simply too far a bridge for 99% of people.
### Integration Ecosystem
The integration ecosystem remains unmatched. With roughly 4500 built-in integrations that abstract away authentication, n8n handles the glue work behind the scenes effectively. This abstraction layer saves significant effort even when using tools like Claude Code.
The New Reality
The gap between n8n and these coding agents used to be a crack — now it's a canyon, and it's widening.
But that doesn't mean n8n is dying. It simply has a very specific lane now: simple projects, non-technical teams, and integration-heavy backend automation.
Six to twelve months ago, the highway was dominated by n8n. Now it just has a couple of lanes on the right-hand side — and that's fine. The key is being tool-agnostic and solving problems with the correct tool for the job.
"You should be somewhat tool agnostic. We just want to solve problems and use the correct tool for the job."
The true power comes from knowing how to do both — understanding when each approach makes sense is what separates mature AI developers from one-trick ponies.
Bottom Line
Chase's strongest argument is the practical shift in capability: agentic coding tools have crossed a threshold where complex projects are now handled more elegantly with code than visual workflows. His biggest vulnerability is that n8n still excels at simple automations and remains far superior for non-technical teams — both of which represent a massive portion of real-world use cases. Watch for the tension between these two realities as AI tools continue evolving rapidly.", "pitch": "The author makes a surprising claim: after building hundreds of n8n workflows and teaching thousands, he's now telling people to stop using it mostly — not because it's bad, but because something fundamental has changed in just six months that altered when it's the right tool. This shift matters because tools like Claude Code have crossed a threshold where complex AI automation is now faster, more scalable, and legally clearer to build commercially — which directly affects anyone building AI systems for themselves or clients.", "pull_quote": "You should be somewhat tool agnostic. We just want to solve problems and use the correct tool for the job.