n8n vs Windmill: Which Automation Tool Wins in Real-Life Tests?

Choosing between n8n and Windmill comes down to how you prefer to build and run automations. Both are open-source, powerful, and rapidly growing in popularity, but they approach workflows in very different ways.

In this comparison, I’ll walk you through how each platform performs real-world workflow tests. By the end, you’ll know which tool makes the most sense for your use case.

n8n vs Windmill: Quick Summary

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Quick Overview of n8n and Windmill

What is n8n?

n8n is an open-source workflow automation platform designed for technical teams. It combines drag-and-drop simplicity with the option to write code, making it ideal for building multi-step AI agents and complex data workflows. You can self-host for full control or use its secure cloud service.

What is Windmill?

Windmill is an open-source developer platform and workflow engine designed to turn scripts into production-grade apps, APIs, and workflows. Supporting multiple languages like Python, Go, and Rust, it enables teams to build, test, and deploy internal tools, data pipelines, and automations quickly with reliability, observability, and easy self-hosting.

1. Sign-Up and Onboarding

I always start by looking at the sign-up and onboarding process. This step is crucial because it sets the tone for how approachable the platform is.

My Experience with n8n

n8n offers two primary ways to get started: you can either use their hosted service at n8n.cloud or self-host it on your own infrastructure. For my first run, I decided to try the cloud-hosted option because I wanted to see how quickly I could get inside and start experimenting.

I went straight to the n8n homepage and hit the bright “Get started for free” button.

The registration form was simple: full name, company email (entered twice), password, and account name: The account name also doubles as your subdomain (e.g., yourname.n8n.cloud), which I thought was a nice touch for clarity. What stood out immediately was that no credit card was required, just the essentials to start a 14-day free trial.

Once I submitted the form, I was dropped directly into the dashboard. It was refreshingly minimal: just a top menu with Dashboard, Manage, and Help Center. My trial status was clearly displayed at the top (14 days left, 1,000 executions per month), along with a large “Open Instance” button.

Clicking this instantly launched me into the main workspace, where the actual workflow building happens.

I liked that there weren’t any distracting pop-ups, forced tutorials, or hand-holding overlays. If you’re like me and want to dive in right away, n8n lets you do that. Within minutes, I was inside the Workflow Dashboard, ready to drag nodes, connect APIs, and test automations.

That said, n8n also emphasizes self-hosting, which I explored after the cloud trial. You can run it with npm, Docker, or on cloud providers like AWS or DigitalOcean.

Self-hosting gives you complete control over your environment, unlimited executions, and stronger data privacy: But it does come with responsibility—managing servers, updates, and security patches.

If you’re comfortable with Docker and server administration, it’s a great option. If not, n8n Cloud removes all that complexity.

Additionally, if you plan to self-host, you’ll need reliable servers. Here’s a list of the best n8n hosting providers to help you choose the right setup.

My Experience with Windmill

Windmill, on the other hand, gave me a slightly different onboarding journey. On the homepage, I clicked “Try Windmill Cloud”, which took me straight to a login/signup screen.

Here I didn’t need to fill out a form. Windmill supports single sign-on with GitHub, GitLab, Google, or Microsoft. I chose my Google account, and within seconds, I was signed in. That’s how quick it was. “No password creation, no waiting for email confirmations.”

Right after login, I landed on a workspace selection screen. Windmill makes collaboration a central part of its experience. I had the choice to join existing workspaces already tied to my email, accept invites, or create a brand new one. This immediately highlighted Windmill’s team-oriented approach.

Inside the dashboard, the UI felt clean yet feature-rich. A left sidebar gave me access to everything—Runs, Variables, Resources, Assets, Triggers, and more. At the top, large “+ Script,” “+ Flow,” and “+ App” buttons made it obvious how to start building.

What really impressed me was the pre-loaded templates and examples in the main content area. I could fork workflows like “Enrich CRM contact details from email” or “Ban a user and notify them via Slack” right out of the gate. This made onboarding feel not just easy, but productive from the very first click.

In terms of self-hosting, Windmill is also open-source and gives you several options.

• For smaller setups: you can use Docker and Docker Compose
• For larger production environments: they recommend deploying on Kubernetes with their Helm chart

Requirements: A Postgres database is required, and you’ll need containers running in both server and worker modes. Compared to n8n, the requirements felt a little more technical upfront, but the payoff is clear. You get total control over your data, scalability, and cost savings.

Teams under 10 users can even self-host Windmill for free with some limitations, making it attractive for small dev teams.

2. Visual Editor and Workflow Design

When I test workflow automation platforms, I want to know how easily and intuitively I can actually build something.

Building My Email Triage Bot in n8n

For me, that problem is email overload. My inbox is constantly flooded with invoices, tickets, and urgent messages, and I spend way too much time sorting through them.

So instead of setting up a toy “form submission” flow, I decided to build something I would genuinely use every day: an Email Triage Bot.

I started with the Gmail Trigger node to monitor my inbox in real time. n8n makes this step simple—once you connect Gmail, you can click “Fetch Test Event” to pull in a few recent emails.

These test emails become the sample data for the rest of your workflow, so you don’t have to guess field names—you see exactly what’s available (sender, subject, snippet, date, etc.).

Next, I dropped in a Switch node to act as the decision-maker. Using the subject and snippet fields from Gmail, I set up rules like:

• If subject contains “invoice” → go to Invoice Branch
• If it contains “job” → go to Job Branch
• If it contains “urgent” → go to Urgent Branch
• Everything else → General Branch

This was where n8n really started to feel powerful. Instead of me scanning my inbox, the bot automatically sorts emails based on content.

For invoices, I kept it simple. Each email was logged into a Google Sheet called “Email Logs”, with fields like Date, From, Subject, Snippet, and Category = “Invoice.” That way, finance or I could quickly see every invoice that came in without digging through Gmail.

Here’s where I brought in AI. For job-related emails, I connected a Gemini node with a prompt: “Summarize the job posting in 2 sentences and classify it as Inquiry, Offer, or Other.”

The result was added to the Google Sheet under ai_summary. Now instead of reading through long job postings, I could skim the summaries directly from my sheet.

Urgent emails got special treatment. In addition to logging them in Google Sheets, I sent a formatted Slack/Telegram alert with the sender, subject, and timestamp. That way, I get notified in real time when something critical lands.

Everything else went into the “General” bucket, still logged to the same sheet. This gave me a central searchable archive of all my emails.

How n8n’s Data Flow Works:
What makes this possible is the way n8n handles data between nodes. Every time a node runs (like Gmail fetching an email), it outputs the result in a standardized format.

• Array of Items: Data is passed as an array, which might contain one or many “items.”
• Each Item = One Record: For example, each email from Gmail is its own item.
• JSON Wrapper: Inside each item is a json key that holds the actual data.

{ “json”: { “from”: “john.doe@example.com”, “subject”: “Invoice #123”, “snippet”: “Attached is the invoice for services…” } }

• Mapping Fields: When you add a new node (like a Switch or Google Sheets), you don’t have to manually type field names. Because you already pulled test data in Step 1, n8n shows you the available fields from the trigger. You just drag and drop the values into the new node’s input.
• Multiple Items: If Gmail pulled in 3 emails, n8n sends an array with 3 items, and each downstream node processes them one by one.

This consistent structure is why workflows remain predictable and why branching, mapping, and AI calls worked seamlessly in my Email Triage Bot.

Windmill’s Visual Editor and Workflow Design

After getting through sign-up and onboarding, the next thing I wanted to test was how Windmill handles real workflow design.

With n8n, I had already built my Email Triage Bot for managing Gmail overload. With Windmill, I wanted to push the same idea: “can I recreate a flow that pulls emails, classifies them, logs them, and maybe triggers alerts?”

But before diving into a full triage bot, I decided to break it down into steps: write a script, turn it into a flow, and then see how far I could take it with Windmill’s app builder.

From the dashboard, the big “+ Script” button jumped out. Clicking it dropped me into Windmill’s code editor. Unlike n8n, which is primarily node-based and drag-and-drop, Windmill starts with code first—but it doesn’t leave you stranded. The editor was split:

• Left side: code editor, pre-filled with a Python template.
• Right side: settings panel with tabs for metadata, runtime options, caching, triggers, and an auto-generated UI.

This layout made it clear that Windmill wants developers to feel at home while still offering low-code enhancements.

I kept things simple at first. The default script printed a welcome message using parameters like name and age. On the Generated UI tab, I saw Windmill had already created form fields for those inputs—string, integer, even a file upload. That meant I could test the script without touching the code.

When I hit “Test”, Windmill spun up the job instantly. The logs streamed on the right, showing dependency installation (requests, urllib3, etc.), memory usage (peaked at ~71 MB), and my print statements in real time. The Result tab showed a neat JSON output of my script’s return values.

This immediate feedback loop was impressive. It felt like a bridge between raw scripting and production-grade automation. You could iterate, tweak, and re-run without leaving the screen.

With a working script, I wanted to chain steps together, the way I had done in n8n with Gmail → Switch → Google Sheets → Slack. So, I clicked “+ Flow.”

Windmill greeted me with a guided tutorial: an 18-step walkthrough that explained inputs, adding nodes, connecting outputs, and writing inline code. At first, I thought it would feel tedious, but it actually helped me understand how Windmill thinks about flows: every step is essentially a script, and the canvas orchestrates them.

Here’s what I built to simulate part of my Email Triage Bot:

1. Trigger Input: Instead of Gmail, I started with a text input field to simulate an email subject.
2. Branching Step: I added a TypeScript (Bun) node that checked keywords like “invoice” or “urgent” and routed the output accordingly.
3. Storage Step: For classified emails, I set up a PostgreSQL resource to log them. Windmill let me configure the DB connection in the Resources section, and the node automatically exposed a schema for my SQL insert.
4. Notification Step: Finally, I added a Slack resource. Windmill already had Slack as a built-in integration, so I authenticated it once and reused it. The flow sent a simple message if the keyword was “urgent.”

When I clicked Run, Windmill’s Graph tab came alive. Each node lit up in sequence, showing how long it took (e.g., 0.4s for SQL, 0.3s for Slack). If something failed, I could click into the Logs tab for that exact node and see the error in plain detail.

This was where Windmill felt more developer-grade than n8n. n8n’s mapping UI is smooth, but you’re often locked into predefined node options. In Windmill, because each step is essentially code with an auto-generated schema, I had far more control—if I needed to preprocess a string, parse JSON, or add retries, I could just write it inline.

To push further, I opened Windmill’s example flow: “Ban a user, notify them by email, and us by Slack.” This was connected to Postgres, pulled data, branched into email and Slack notifications, and then converged results.

The Graph view showed a tree of parallel paths:

• Database update.
• Email notification branch.
• Slack reporting branch.

Each branch had execution times and dry-run markers if resources weren’t configured. This visual was a game-changer. It gave me a precise, high-level overview of how conditional logic executed, but I could still drill into any step for details.

The Logs tab was equally clear: I could see SQL queries, email payloads, and warnings (like a TLS certificate issue with Postgres). For debugging complex automations, this level of observability is invaluable.

Finally, I wanted to see how Windmill’s Apps feature extended workflows into user-facing tools. I opened the demo app “Cooking Recipes.”

On the left, there was a sortable table of recipes; on the right, a details pane with ingredients, nutrition, and even images. At the bottom, a form let me share a recipe via Gmail. Behind the scenes, it was just flows and resources, but the presentation felt polished.

To test, I added a Gmail resource, completed an OAuth flow, and sent myself a recipe. It worked exactly as promised. This confirmed that Windmill isn’t just for backend scripts. It can expose those workflows to end users with a functional UI, no React coding required.

Windmill’s editor feels so powerful:

• Scripts let me bring in logic from any language.
• Flows let me orchestrate multi-step pipelines with branching and integrations.
• Apps let me put a UI on top, so workflows aren’t just background jobs but actual tools for teams.

Compared to n8n, the learning curve is steeper because you’re working with code more often. But in exchange, you get precision, flexibility, and deep observability. Where n8n shines at drag-and-drop speed, Windmill shines at turning raw scripts into production workflows with full transparency.

3. Debugging and Testing

Building automations is exciting, but in real life, things rarely work on the first try. That’s why I always pay close attention to debugging and testing tools because a platform is only as good as its ability to help you fix what’s broken.

Debugging in n8n

To really test n8n’s debugging, I ran a more complex workflow designed to generate AI content. As soon as I hit “Execute workflow”, the workflow started running, but one of the AI Agent nodes turned red on the canvas.

At the same time, a clear pop-up error appeared, telling me not just that the “AI Agent” failed, but specifically that the problem was in a sub-node: “LLM: Generate Raw Idea (GPT-4.1)”. The error even included a 404 status code and a troubleshooting URL from the underlying LangChain library.

That level of specificity is a time-saver. Instead of guessing, I knew exactly which part of the chain was broken.

At the bottom of the screen, n8n’s debugging panels came into play:

• Logs panel (bottom left): Showed the trigger succeeded, but the AI Agent node failed. I could expand the failing step and drill down into the sub-step where the error occurred.
• Output panel (bottom center): Clicking on the node showed the full error message: “The resource you are requesting could not be found.” It even had an Ask Assistant button for extra help.

What I liked most was the option to re-run only the node that failed. After correcting the model name, I didn’t have to rerun the whole workflow. I just clicked “Execute step” on the AI node.

Another strength: the Executions log. Every run is stored permanently, and I could open a failed run in a read-only view to see exactly what happened in that moment.

For production scenarios, n8n even lets you set up an Error Workflow. This is a separate automation triggered whenever another workflow fails. I connected mine to Slack, so now I get notified the moment something breaks in the background.

Debugging in Windmill

Windmill takes a different approach. It’s more developer-first, but no less powerful.

When I was writing scripts, the Test button became my constant companion. One click and the script ran instantly, with the right-hand panel splitting into:

• Logs: A detailed, chronological trace. I could see dependency installs, runtime events, and even exact TLS errors when a database connection failed.
• Result: The JSON output of the script, making it easy to check if my code returned the expected structure.
• Metrics: Memory usage over time—great for spotting performance bottlenecks.

This tight feedback loop meant I could iterate quickly, and because the logs captured both my print statements and system events, I always knew where the problem was.

With flows, debugging got even more visual. After hitting “Test flow,” the Graph tab lit up, showing each node’s status in real time: running, success, or failed. For example, when I tested the “Ban a user, notify them by email, and us by Slack” example, I could literally watch the decision tree unfold—database updates, Slack notifications, email branches—all timestamped and colored by status.

When one branch failed, I clicked on it and instantly saw the logs specific to that node.

One of Windmill’s standout features is the ability to restart from a specific step, iteration, or branch: Instead of rerunning the entire workflow (which can be resource-heavy), I could restart exactly where things went wrong. That’s invaluable for complex, data-heavy pipelines.

For apps, Windmill adds a Troubleshoot panel and preview mode, letting me debug UI issues just as easily as backend logic. If the JSON configuration of a component was off, the Troubleshoot panel caught it immediately.

4. Integrations and AI Capabilities

I need tools that can handle both everyday business apps (Slack, Google Sheets, Gmail) and systems-level integrations (databases, APIs, custom services).

And with AI becoming central to how workflows are designed, I also wanted to see how each platform incorporates AI.

n8n: Depth, Breadth, and AI as a First-Class Citizen

n8n sets the bar high here. It offers over 1,100 pre-built integrations, one of the largest libraries of any workflow automation platform I’ve tried.

• Everyday apps covered: I had no trouble dropping in Gmail, Slack, Notion, and Google Sheets to handle daily productivity tasks.
• Systems-level support: What sets n8n apart is that it doesn’t stop at surface-level connectors. It goes deeper, with databases (Postgres, MySQL, MongoDB), developer platforms (GitHub, GitLab), and low-level protocols (Webhook, GraphQL, HTTP Request).
• Granularity: Instead of offering only a few high-level actions, many n8n nodes expose dozens of API endpoints.

Where n8n really blew me away was in AI capabilities. It makes it part of the platform’s architecture. The AI category of nodes includes:

• Language Models: Connect directly to OpenAI, Anthropic, Google Gemini, and others.
• Agents: Build autonomous agents that can reason, use tools, and make decisions mid-flow.
• Memory: Give those agents context, so they remember past inputs and interactions.
• Vector Stores and Embeddings: Essential for Retrieval-Augmented Generation (RAG) workflows.
• Document Loaders and Output Parsers: Building blocks for transforming unstructured data into usable form.

I put this to the test by enhancing my Email Triage Bot. With n8n, I added a Gemini node that summarized job-related emails and classified them into “Inquiry,” “Offer,” or “Other.”

The fact that I could drag this in as a pre-built node, without writing Python or LangChain code, showed how seriously n8n takes AI as a first-class feature.

For businesses experimenting with AI-driven processes, such as customer support agents, content pipelines, or RAG search systems, this level of integration is a game-changer.

Windmill: Fewer Pre-Built Connectors, More Developer Freedom

Windmill takes a very different approach. Out of the box, it offers over 50 pre-made integrations for popular services, including Slack, GitHub, Google Sheets, Stripe, Supabase, and Airtable. These cover the basics, but it’s nowhere near n8n’s 1,100+ library.

Instead, Windmill is API-first and developer-centric:

• Resources and Resource Types: In Windmill, an integration is called a “Resource.” Each resource is defined by a schema (e.g., a Slack resource might require a token and channel). You can configure these once and reuse them across scripts and flows.
• Extending integrations: If something isn’t available, you don’t wait for the platform. You build it yourself. Windmill lets you define a new Resource Type in JSON schema, then share it to WindmillHub so others can use it.
• API in code: Because Windmill supports Python, TypeScript, Go, Rust, and more, you can always import SDKs or libraries directly.

When it comes to AI, Windmill has OpenAI integration ready to go, and because it’s script-based, you can easily bring in LangChain, HuggingFace, or other AI frameworks.

But unlike n8n, there aren’t pre-built nodes for “Agents,” “Memory,” or “Vector Stores.” If you want those, you build them yourself inside a script.

This means Windmill’s AI support is flexible but DIY. For developers, you’re not locked into someone else’s abstraction. But for teams who want plug-and-play AI without writing code, n8n is far ahead.

5. Pricing and Scalability

n8n: Predictable, Execution-Based Billing

n8n has one of the cleanest and most predictable pricing models I’ve seen.

The core idea is “you can create as many workflows as you want, with as many steps as you want; you only pay when they run.”

• Execution-based billing: One “execution” equals one complete run of a workflow, from trigger to finish, no matter how many nodes are inside.

• For example, a 5-node workflow (trigger → fetch data → transform → send email → log result) that runs once will be 1 execution.
• Even a 20-node monster workflow that runs only once will still be considered one execution.

This is a huge advantage for complex automations, since you’re not penalized for sophistication.

• Cloud Plans:

• Start at $20/month for the Starter tier.
• All plans include unlimited workflows, unlimited users, and unlimited steps.
• Every plan comes with a 14-day free trial; no credit card required.
• Pricing scales only with executions.

• Self-Hosted Plans:

• Community Edition: 100% free, unlimited executions, but you manage hosting, security, scaling, and updates yourself.
• Business/Enterprise: Paid licenses for advanced features like SSO, premium support, and enterprise-grade scaling.

While the software itself is free, you need infrastructure (cloud servers, Docker/Kubernetes setup, monitoring, backups). For small teams with DevOps know-how, this is a bargain. For less technical users, the cloud-hosted plan is usually worth it.

Scalability: n8n is designed to scale horizontally, whether on its cloud or your own servers. Since pricing is tied to executions, you can run incredibly complex workflows without cost surprises, as long as execution counts remain predictable.

Windmill: Compute Units and Seats

Windmill’s pricing feels more like a developer cloud platform than a workflow tool. Instead of executions alone, it charges based on two dimensions:

1. Seats: The number of active users (developers vs operators).

• Developers = full creators of scripts/flows.
• Operators = limited users who can only run workflows.
• 2 operators = 1 developer seat in pricing.

2. Compute Units (CU): A measure of how much memory-backed worker capacity you provision.

• 1 CU = 2 GB of worker memory per month.
• A standard worker (2GB) = 1 CU.
• Larger workers (>2GB) = 2 CU.
• Workers are billed based on provisioned memory, not actual usage.

Cloud Plans:

• Free Tier: $0/mo. Includes 1,000 executions/month, unlimited variables/resources/flows, and SSO (Google, GitHub, etc.). A generous way to try the platform.
• Team Plan: From $10/mo per developer, with 10k executions/month and 7-day audit logs. Great for small teams.
• Enterprise: From ~$840/mo, with unlimited executions, isolated infrastructure, unlimited audit logs, 24/7 support, and up to 26M jobs per vCPU per month. Pricing here is customized based on seats + compute units.

Self-Hosted Plans:

• Free Community Edition: $0/mo, unlimited executions, up to 10 users with SSO.
• Enterprise Self-Hosted: From $120–$170/mo for 1 developer, plus compute units. Includes commercial license, unlimited audit logs, priority support, and enterprise-only features (SAML, SCIM, dedicated clusters).

Scalability: Windmill is highly scalable thanks to its worker-based architecture. You can allocate compute units flexibly across worker groups, autoscale them minute by minute, and run up to 26M jobs per worker/month.

This makes it ideal for organizations with spiky workloads or who need to tightly control infrastructure costs.

But here’s the trade-off: pricing is more complex than n8n’s. You have to think about both who’s using the system and how much memory you provision. That’s natural for developer-centric teams, but less predictable for small businesses.

6. Support and Community Experience

My Experience

n8n

When I tested n8n’s support channels, the forum stood out. I followed a thread where a user was struggling with graph plotting inside n8n.

Carolyn, another forum member, quickly jumped in and shared a detailed solution using a JavaScript charting library inside n8n’s Code node, complete with code samples. Within an hour, the original poster confirmed it worked for them and promised to report back after further testing.

This is what makes n8n’s forum powerful. You might not always get an immediate fix from the official team, but the community itself is deeply technical and responsive. Add to that their documentation (covering everything from Docker hosting to chaining AI agents with memory and vector stores), and you’ve got a platform where self-service troubleshooting is not just possible, it’s reliable.

Windmill

Windmill gave me a different kind of experience. Their Discord server felt almost like live support. I joined and within minutes saw users getting real help. One user had issues with database connections, and a team member (rubenf) didn’t just give a generic answer. They explained why the issue was happening, suggested avoiding pooled connections, and even recommended switching away from Neon to RDS or local Postgres for better performance.

That’s the kind of hands-on guidance you rarely see without paying for enterprise support.

Additionally, their GitHub repository is highly active, with open issues and pull requests demonstrating genuine transparency in ongoing development.

It feels like if you hit a serious problem, you can either go through Discord for fast, practical advice or GitHub for formal issue tracking, and you’ll be heard in both places.

Who Wins? Our Recommendation

After testing both platforms in real workflows, n8n comes out on top overall. The reason isn’t just its open-source flexibility, but how well it handled a real-world use case I built.

An Email Triage Bot that classified, summarized, and logged my Gmail messages in real time. n8n gave me branching logic, AI integration, Slack alerts, and centralized Google Sheets logging, all stitched together without writing custom code.

Its documentation walked me through every step, and the community forum demonstrated real problem-solving in action, with users sharing production-ready fixes, such as integrating charting libraries in the Code node. Windmill’s Discord support was fast and developer-driven, which I loved, but it doesn’t yet match the breadth of workflows, maturity of integrations, and proven community scale that n8n offers. If you need a platform that balances power, extensibility, and reliable user support, n8n is the safer long-term choice.