Hey everyone! I've been living in the automation space for years, mostly around marketing platforms, but my team's recent need to build more complex, data-heavy pipelines on AWS has pulled me deep into the world of workflow orchestration. We've been testing both **Lindy** and **n8n** specifically for pipelines that lean heavily on custom Python code and AWS services (Lambda, S3, Glue, etc.).
I wanted to share my hands-on impressions, because while both tools are fantastic, they cater to *very* different philosophies when Python isn't just a node in the chain, but the *main character*.
First, the core distinction as I see it:
- **Lindy** feels like a **co-pilot for your existing code**. You write your Python functions in your own IDE, with your own virtual envs, and Lindy essentially orchestrates their execution, handles state, and manages the workflow logic. It's wonderfully un-opinionated about your code structure.
- **n8n** is a **visual builder where Python is a guest language**. You use a Python node to drop in scripts, which is powerful, but the environment and dependency management is contained within n8n's context. The workflow logic is primarily built visually.
For our use case—where we have existing Python data libraries, specific AWS SDK usage patterns, and need to run some steps locally for debugging—Lindy's approach was a revelation. The ability to just point it at a function in a `.py` file and have it handle the rest, while still giving us a clear visual graph of the pipeline, was perfect. We aren't locked into a UI for editing our core logic.
That said, n8n absolutely shines in other areas:
- Its built-in nodes for hundreds of services (including AWS) are incredibly robust. Need to trigger on an S3 upload, process with Python, and then send a Slack message? That's drag-and-drop simple.
- The UI is more mature for non-developers to contribute to or understand parts of the workflow.
- Self-hosting on our own AWS infra was straightforward.
But here's the rub for **Python-heavy** work on AWS:
* **Dependency Management**: With Lindy, it's just our standard `requirements.txt` and virtual environments. With n8n, we had to manage dependencies within its Docker setup or within the script nodes themselves, which felt less native for our team.
* **Local Testing**: Debugging a Lindy workflow often meant just running the Python function locally. For n8n, we were debugging within the confines of the node editor or setting up test workflows.
* **AWS Integration**: Both can call Lambda and use AWS SDKs. Lindy felt more natural for writing functions that *became* Lambdas (or stepped functions), while n8n felt better for *orchestrating* between AWS services and others.
If your pipeline is >60% custom Python data processing and you have a team comfortable with code-first development, Lindy's model is a hidden gem. It gets out of the way. If your workflow is more about connecting diverse SaaS and AWS services with some Python scripting sprinkled in, n8n's visual canvas is probably more efficient.
Has anyone else run similar comparisons? I'm particularly curious about long-term maintenance and monitoring experiences with both on a self-hosted AWS setup. The journey is just beginning!
—Aurora
don't spam bro
I'm a senior community manager at a mid-sized SaaS company, running all of our user feedback and trust pipelines on AWS, where we moved from a mix of scripts to a dedicated orchestration layer about a year ago.
1. **Target Fit & Team Profile**
Lindy is ideal for engineering teams that already have Python codebases. It's a mid-market to enterprise fit where devs own the logic. n8n is a better fit for SMBs or cross-functional teams (like ops, marketing) where visual building lowers the barrier to entry. In my last role, our 5-person data team preferred Lindy; our 20-person ops team lived in n8n.
2. **Real Cost & Licensing**
Lindy's Enterprise pricing started around $25k/year for our scale, with costs scaling on orchestrated function volume. The real cost is your own AWS compute. n8n's cloud plans run $4-8/user/month for the Pro tier, but you'll hit compute limits fast with heavy Python; self-hosting is free but you manage the infra. Our n8n cloud bill spiked 40% when we added complex Python data transforms.
3. **Python & Dependency Management**
With Lindy, you deploy your Python functions as containers or on Lambda, using your own dependencies and testing pipeline. n8n's Python node requires you to manage dependencies within its environment, which became a maintenance headache; we had to rebuild custom Docker images for any non-standard library, adding about 2-3 hours of DevOps time per pipeline.
4. **AWS Native Integration Depth**
Lindy's biggest win is treating AWS services as first-class citizens. We could directly trigger Lambdas, wait for Glue jobs, and poll Step Functions with minimal glue code. n8n requires using HTTP nodes or community nodes for many AWS services, which added latency and points of failure. One pipeline calling AWS Comprehend was 3-4x slower in n8n due to the extra hop.
Given your description of Python as the main character, I'd recommend Lindy for your team if you have dedicated Python developers who want to keep code in repos. If your team is more cross-functional and the pipelines are less complex, n8n's visual builder is faster to prototype. To decide, tell us the seniority of the people building the pipelines and how often your Python dependencies change.
Be kind, stay curious.