I’ve been exploring N8N lately and I’m genuinely curious about its advantages compared to writing Python scripts. While N8N seems like a powerful tool, I keep thinking that most of the workflows I create could probably be built using Python with some help from AI assistants like ChatGPT or Claude.
Maybe I’m missing something important here. Are there specific scenarios where N8N really shines compared to custom Python code? I’m wondering if there are features or capabilities in N8N that would be difficult or impossible to replicate with Python scripting.
What are your thoughts on this? Have you found situations where N8N was clearly the better choice over writing your own automation scripts?
From my experience, N8N becomes invaluable when you need to maintain and scale automation workflows over time. Python scripts tend to become technical debt quickly - you write something that works perfectly, then six months later you’re staring at your own code trying to remember what it does. N8N’s visual interface creates self-documenting workflows that are immediately comprehensible. The real game-changer is deployment and monitoring. With Python, you’re managing dependencies, server environments, error handling, and logging infrastructure yourself. N8N handles all of this out of the box with proper retry mechanisms, webhook endpoints, and execution history. I’ve spent countless hours debugging Python automation scripts that broke due to environment changes or API rate limits, while N8N workflows just keep running with built-in resilience features.
tbh, it’s about ease of use and managing workflows. python can be powerful, but if someone else needs to jump in, they might struggle with your code. n8n’s visual setup makes it way simpler for non-devs to see what’s happening and make changes without hassle.
Speed of development is where N8N really differentiates itself from Python scripting. When you need to connect multiple APIs and services, N8N has pre-built connectors that handle authentication, rate limiting, and error handling automatically. Building the same integrations in Python means researching each API’s documentation, writing authentication flows, and implementing proper error handling from scratch. I’ve found that what takes me 2-3 hours to build in N8N would easily require a full day or more in Python when you factor in all the boilerplate code and testing. The visual workflow editor also makes it much easier to handle complex conditional logic and branching scenarios without getting lost in nested if statements and try-catch blocks.