So I've been deep in the weeds lately trying to automate some gnarly data pipelines that live on AWS. The core logic is very Python-heavy—think custom API transformations, pandas for cleaning, and loading to Redshift. The debate internally is whether to build this on Lindy or n8n, and I'm honestly torn.
On paper, n8n seems like the obvious choice for low-code orchestration. It's self-hostable, has tons of pre-built nodes, and is great for stitching services together. But when your core logic is already written in Python, you end up wrapping everything in "Execute Command" nodes or custom script nodes, which feels a bit clunky. The workflow UI becomes more of a scheduler than a true builder.
Lindy, with its native Python agent creation, feels more aligned. You can just write your functions and let the agent handle the orchestration and error handling. For data pipelines, the ability to have an "agent" that understands the context of a failure and can retry or notify seems powerful. But I'm less clear on how it handles complex dependencies between tasks or scheduling at scale on an AWS EC2/ECS setup.
Has anyone run a similar comparison for data-heavy, Python-centric workflows? I'm particularly curious about:
- How each handles state management between pipeline steps (e.g., passing a dataframe's location or a large payload).
- Observability and logging when things inevitably break at 2 AM.
- The operational overhead of maintaining each on AWS infrastructure (EKS vs. EC2, etc.).
My gut says Lindy for more adaptive, logic-heavy workflows, and n8n for when you're integrating a dozen standard SaaS APIs. But for this hybrid case, I'm still evaluating. Would love to hear if anyone's been down this path.
I'm a data team lead at a mid-sized e-commerce platform where we run several internal and customer-facing data pipelines on AWS, all Python at their core. We've had both n8n and Lindy in production for different stages of the same pipeline over the last 18 months, so I've lived through the trade-offs.
**Core comparison:**
1. **Native Python handling:** Lindy's agents treat your Python functions as first-class citizens. You define them, and the agent manages execution, state, and retries within that code's context. In n8n, your Python exists inside generic "Execute Script" nodes. The difference is failure logic; Lindy's agent can often reason about the error from inside your function, while n8n's node just knows the script failed, losing context. For our custom pandas transformations, this meant Lindy could auto-retry on specific data quality exceptions, while n8n required us to bubble everything up as custom status flags.
2. **Orchestration complexity:** n8n wins for strict, multi-service DAGs. Its UI makes dependencies between steps (fetch from S3, transform, load to Redshift, trigger a Slack alert) visually clear and easy to modify. Lindy's agent-based model is more linear; chaining complex tasks where one step branches into three parallel processes is doable but feels more manual. We hit a ceiling where a pipeline with over 15 interdependent steps became harder to reason about in Lindy's interface.
3. **AWS hosting & scaling cost:** Self-hosted n8n on an EC2 (or ECS) t3a.large ran us about $70/month per instance, handling ~20 concurrent workflows. Lindy's pricing is per agent, and for similar throughput we were in the $4-8/agent/month range, but the hidden cost is compute. Since Lindy agents are persistently alive waiting for triggers, our AWS Fargate costs for the container hosting were closer to $90/month for the same workload. The total bill was comparable, but the cost centers shifted.
4. **Operational overhead:** n8n requires you to manage the low-code workflow definitions, the worker queue, and your Python code as separate entities. It's more pieces, but each is isolated. Lindy collapses this into just your Python code and the agent config, which is simpler until you need to debug why an agent hung. Our team spent more time on n8n setup initially, but less on mysterious production incidents compared to Lindy, where a memory leak in a pandas operation could quietly stall an agent.
**My pick:**
If your pipelines are fundamentally a sequence of Python scripts where each step is a single, substantial chunk of code, I'd recommend Lindy. Its model aligns perfectly and reduces boilerplate. If your "Python-heavy" pipeline still involves many small, discrete steps, conditional branching based on file presence in S3, or integrating with a dozen other AWS services directly, n8n's visual workflow will save you more headaches. To make it clean, tell us: how many distinct logical steps are in your gnawliest pipeline, and do you need to coordinate parallel execution often?
Try everything, keep what works.
Exactly - that's the clunky feeling I get too. The n8n UI becomes a visual wrapper you have to manage, which adds overhead if your real logic is in scripts anyway.
For your AWS setup, I found Lindy's scaling a bit manual. You define your agent and containerize it, but handling task dependencies between different agents feels less declarative than a classic DAG. It works, you just orchestrate it at a higher level.
Since you're already in Python, maybe try a quick PoC with one messy pipeline in each? The local dev experience for each tells you a lot.
You're right to focus on the core issue: wrapping Python in a low-code wrapper defeats the purpose.
Your hesitation on Lindy's scheduling and dependencies is valid. It's agent-centric, not task-centric. You manage dependencies by having agents trigger other agents or use external event systems. It's a different mental model than a classic orchestration DAG. If your team thinks in linear pipelines, that's friction.
For your AWS setup, the real question is who owns the infra. With n8n, you're managing the n8n instance plus your Python execution environment. With Lindy, you're building and managing individual agent containers. Which overhead is your team better equipped to handle?
Skip the PoC on a simple pipeline. Test the failure mode of your *gnarliest* transformation step in both. That's where the rubber meets the road.
> the real question is who owns the infra.
You're dancing around the real cost. It's not about which overhead your team can "handle." It's which one quietly doubles your monthly bill.
A single n8n instance on a t3.large for scheduling, plus your actual compute. That's two resources always on. With Lindy's agents, you can actually scale the compute to zero when idle and use Spot for the containers. My team's "gnarliest" step runs on Spot instances via Fargate. It fails sometimes, but the retry logic is built in and it costs 70% less. The failure mode is cheaper.
show the math
You make a strong practical point on cost, especially the compute-to-zero potential. That's a key operational advantage that's easy to overlook in feature comparisons.
The "failure mode is cheaper" argument is compelling for AWS-native setups. However, doesn't that hinge entirely on the reliability of the built-in retry logic within the agent's code? If a custom transformation has a complex failure state, you might be trading infrastructure cost for increased development time hardening that logic.
How do you quantify that trade-off for your team? Is the savings still clear if you factor in the extra cycles spent writing more resilient Python functions versus configuring retries at the orchestration layer in n8n?
Yeah, the mental model shift to agent-centric is the real hurdle, isn't it? If your team's brain is wired for linear DAGs, retraining that can be a bigger cost than any infra bill.
But I wonder, for AWS, isn't that friction also a forcing function? It pushes you to design more decoupled, event-driven components, which is kind of the AWS way. Maybe the friction is good? Or just annoying.