n8n vs. Windmill

n8n uses a visual, drag-and-drop canvas where you connect nodes to define how data and actions move through a workflow. Pre-built nodes handle common operations, letting you assemble much of the workflow without writing the code yourself. You can also inspect the logic as a connected flow instead of tracing it across scripts.

When you need custom logic, the Code node lets you write JavaScript or Python directly inside the workflow. That gives developers an escape hatch for transformations, business rules, or other logic that pre-built nodes don’t cover, without moving the entire automation into a separate codebase.

Business tier and up allows teams to use Git-based source control to support environments. Users can link n8n instances to a Git repository to build multiple n8n environments, backed by Git branches. Teams can also use n8n’s Server CLI for import/export, workflow activation, and backups. If interacting with n8n programmatically from a remote device or integrating with AI agents, developers can also use the n8n CLI for importing and exporting workflows, running executions in scripts and CI pipelines, and local development and debugging.
When the standard nodes are not enough, users can fallback to the Code Node and run native JS / Python scripts

Windmill takes a code-first approach. Developers write scripts in languages like Python, TypeScript, Go, PHP, Bash, and SQL, then compose them into larger workflows. Its visual flow editor complements that model rather than replacing it: Code remains the main building block, while the editor shows how individual steps fit together.

Teams can work locally with the CLI and VS Code extension, sync changes with Git, and bring existing scripts into Windmill instead of rebuilding them as visual components. That keeps automation development close to the tools and practices developers already use.
Windmill offers a built-in code editor for several programming and scripting languages

n8n brings AI components onto the same visual canvas as traditional automation steps. With AI agents, you can connect LLMs to tools and business systems so agents can retrieve information, make decisions, and take actions as part of a larger workflow. Memory nodes can preserve conversational context, while human approval steps give teams control over actions that need review.

Teams can also build RAG in n8n by connecting models, vector stores, data sources, and retrieval steps in a single workflow. Native vector store support reduces the amount of external tooling needed to assemble a RAG pipeline, and the same workflow can connect retrieved context to downstream applications or processes. n8n also supports MCP and sub-workflows for more complex agent orchestration.

Windmill approaches AI from a code-first foundation. Developers can build AI workflows with Python or TypeScript and use libraries like LangChain from those ecosystems for model calls, retrieval, agents, and other AI logic. It gives developers the flexibility to choose and implement the libraries they want.

The trade-off is that more of the AI architecture lives in code. But teams comfortable managing AI libraries and writing their own orchestration logic may prefer the added control, particularly when they already have established development patterns around Python or TypeScript.

With n8n, you can usually start with a pre-built integration. The catalog covers 1000+ common business apps, databases, developer tools, and AI services, with authentication and supported actions already built into many nodes. For anything the catalog doesn’t cover, the generic HTTP Request node can connect directly to an API, and developers can build custom nodes when they need something more specific.

Windmill requires developers to do more of the integration work in code. You can import npm, PyPI, and Go packages directly into scripts, which opens up their respective libraries and SDKs without waiting for Windmill to ship a dedicated connector. More than 200 pre-built resource types handle credentials and configuration that scripts and flows need to share. That flexibility also leaves more dependency management in the developer’s hands.

n8n’s RBAC gives users different levels of access based on their role. On qualifying paid plans, SSO, SAML, and LDAP and audit logs cover two other practical needs: managing user logins and access and maintaining a record of activity. n8n also includes encrypted secrets store at every tier and enforced 2FA across the instance and external secret store integration in Enterprise.

Windmill takes a generous approach to governance in its open-source edition. Granular permissions and basic audit logs are included, so access can be managed across scripts, flows, apps, and resources without moving to a commercial tier. SSO and some additional security features depend on the edition.

n8n offers a free, self-hosted Community Edition alongside paid self-hosted plans and n8n Cloud. Instead of charging for every step in a workflow, n8n pricing is based on workflow executions, which means adding more nodes doesn’t increase the execution count. Paid tiers add features for larger teams, including more advanced governance and collaboration options.

Windmill also has a free, self-hosted open-source edition under AGPLv3, with commercial plans available for teams that need additional features and support. Windmill pricing uses compute-based usage for its cloud offering, so cost depends in part on the resources workflows consume. The self-hosted editions separate open-source functionality from features available under commercial licenses.

n8n can run on your own infrastructure or as a managed service through n8n Cloud. For self-hosting, Docker is the simplest starting point, while Kubernetes gives teams more control over larger deployments.

At higher volumes, n8n can run in queue mode. A main instance handles incoming webhooks and workflow orchestration, while separate workers pick up executions from a Redis-backed queue. PostgreSQL stores execution data. And because processing is separated from the main instance, you can add workers as demand grows instead of moving the whole deployment to a larger machine.

You can also self-host Windmill with Docker or Kubernetes or run it through Windmill Cloud. Its scaling model revolves around workers: Jobs enter a queue, and workers pull jobs as capacity becomes available.

Adding more workers increases execution capacity and allows more jobs to run concurrently. Workers can also be grouped to handle different types of workloads.

n8n keeps debugging close to the workflow itself. You can inspect the data entering and leaving each node, rerun individual steps, and pin data while you work on another part of the flow. That makes it easier to isolate a problem without repeatedly running the workflow from the beginning.

Windmill gives developers logs and execution details for scripts and flows, with errors tied back to the code being run. Since scripts are the main building blocks, debugging will feel familiar if your team already spends time working in code and developer tooling.