n8n vs Activepieces vs Windmill: Open-Source Automation 2026

Most workflow automation comparisons lump open-source tools in with closed-source SaaS products like Zapier and Make. That misses the point. If you have already decided to self-host or need source-level control, you are choosing between a fundamentally different set of trade-offs: deployment complexity, language runtime, contributor velocity, and long-term licensing risk.

n8n, Activepieces, and Windmill are the three open-source workflow engines that have crossed the threshold from hobby projects to production-grade platforms. Each targets a different user profile. n8n is the most popular and has the widest community. Activepieces is the newest, built to be the easiest self-hosted alternative to Zapier. Windmill is the most technically ambitious, designed for developer teams that want scripts and workflows to coexist in a single platform.

We have deployed all three for clients at Kanopy and run internal benchmarks on each. This comparison reflects what we learned building production workflows, not marketing pages.

Architecture choices ripple through everything: startup time, memory consumption, concurrency limits, and which languages your team can use to extend the platform.

n8n: Node.js + TypeScript

n8n is built entirely in TypeScript running on Node.js. The backend uses Express and TypeORM, with SQLite as the default database (Postgres for production). Workflows execute in the main Node.js process by default, though queue mode backed by Redis and Bull distributes executions across multiple workers. Custom nodes are written in TypeScript, so teams in the JavaScript ecosystem can extend n8n naturally. Python-heavy teams will need to shell out via the Execute Command node, which adds friction.

Activepieces: TypeScript + Sandboxed Pieces

Activepieces also uses TypeScript and Node.js, but integrations ("pieces") run in sandboxed environments. A broken piece cannot crash the main engine. The backend uses Fastify instead of Express, and the database layer is Postgres-first. The sandboxing model makes Activepieces more resilient to third-party failures, but each piece execution spins up its own context, consuming more memory per workflow run than n8n's in-process model.

Windmill: Rust Core + Polyglot Workers

Windmill is architecturally different. The core engine is written in Rust, handling scheduling, queue management, and the API layer. Workers execute scripts in TypeScript (Deno, not Node.js), Python, Go, Bash, SQL, or PowerShell, each in its own isolated process with configurable resource limits. The Rust scheduler can dispatch thousands of jobs per second with minimal overhead. The trade-off is deployment complexity: you need the Rust server plus language-specific worker images, making the Docker Compose setup more involved than n8n's single container.

All three platforms offer Docker-based self-hosting. The difference is how much work you do after docker-compose up.

n8n Self-Hosting

n8n has the simplest setup. A single Docker container with a Postgres connection string gets you running in under five minutes. For production, add Redis for queue mode and a second container for workers. Upgrades are straightforward: pull the new image, run migrations, restart. In two years of managing n8n instances for clients, we have seen exactly one breaking migration, and the rollback was clean.

Activepieces Self-Hosting

Activepieces ships a single Docker image bundling server and worker. You need Postgres and Redis as external dependencies. The key difference from n8n: Activepieces requires more memory because of its sandboxed execution model. Budget 2 GB RAM minimum for production, compared to 1 GB for a low-traffic n8n instance. The team ships weekly releases, which means fast bug fixes but more frequent upgrade cycles.

Windmill Self-Hosting

Windmill's Docker Compose file includes the server, multiple worker containers (one per language runtime), Postgres, and an optional LSP container for in-browser code editing. The default setup spins up six to eight containers. Resource requirements are higher: 4 GB RAM minimum with all language runtimes enabled. You can trim this by disabling workers you do not need. Windmill also offers Kubernetes Helm charts and Terraform modules for cloud deployment.

This is where the three platforms diverge most sharply, and where your team composition should drive the decision.

n8n: Visual-First with Code Escape Hatches

n8n's canvas editor is the centerpiece. You drag nodes onto a canvas, connect them with wires, and configure each node through a form-based UI. It supports branching, loops, error handling, sub-workflows, and conditional logic all through visual elements. For teams that include non-developers, this visual approach removes the barrier to building moderately complex automations.

When you need code, n8n provides Code nodes that accept JavaScript or Python snippets. You can reference input data, call external libraries (with some restrictions in cloud mode), and return structured output. The experience is good but constrained. You are always writing code inside a node, not building a full application. For most automation use cases, that is exactly right.

Activepieces: The Simplest Visual Builder

Activepieces doubles down on simplicity. The flow builder uses a linear, top-to-bottom layout rather than a free-form canvas. Each step snaps into place below the previous one, with branches expanding horizontally. The result is workflows that are easy to read even for people who have never seen a workflow tool before.

The trade-off is flexibility. Complex workflows with multiple parallel branches or loops within loops can feel cramped in Activepieces' linear layout. The platform added looping and branching in late 2025, but these features still feel less mature than n8n's. If your primary users are operations managers or marketing teams, Activepieces' simplicity is a feature. If your workflows regularly involve 20+ steps with nested logic, n8n's canvas gives you more room.

Windmill: Code-First with a UI Layer

Windmill inverts the paradigm. You start by writing scripts in TypeScript, Python, Go, or SQL. The platform provides a browser-based IDE with full language server protocol (LSP) support: autocompletion, type checking, inline errors. Once your script works, Windmill auto-generates a UI form from the function's typed parameters. You can also compose scripts into flows using a visual editor, but the scripts themselves are the primary artifact.

This approach appeals to developer teams that think in code, not diagrams. You get version control (Windmill syncs with Git natively), proper dependency management (requirements.txt for Python, package.json for TypeScript), and the ability to write unit tests against your scripts. The downside: non-technical team members cannot build workflows without developer help. If you want a platform where product managers create their own automations, Windmill is not the right fit.

All three platforms have added AI capabilities since 2025, but the depth varies significantly. If you are building AI-powered automations, the platform's AI integration maturity matters.

n8n AI Capabilities

n8n ships dedicated nodes for OpenAI, Anthropic Claude, Google Gemini, Ollama (local models), and LangChain. The AI Agent node lets you build tool-using agents directly in a workflow: define tools as sub-workflows, connect them to an LLM, and let the agent decide which tools to call. Vector store nodes for Pinecone, Qdrant, and Supabase enable RAG pipelines entirely within the visual editor.

The AI builder released in early 2026 generates node graphs from natural language descriptions. It handles simple flows well but still needs manual adjustment for complex multi-step logic.

Activepieces AI Capabilities

Activepieces added OpenAI and Anthropic pieces in 2025, plus a generic HTTP piece that works with any LLM API. The AI pieces are functional but basic: you send a prompt, get a response, and pass it to the next step. There is no built-in agent framework, no vector store integration, and no RAG pipeline support at the piece level. You can build these patterns manually by chaining HTTP calls and code pieces, but it requires more effort than n8n's dedicated nodes.

Activepieces does have an AI copilot that helps generate piece configurations and suggests next steps. It is useful for beginners but does not change the platform's AI integration depth.

Windmill AI Capabilities

Windmill takes a different approach. Because scripts are first-class citizens, you use LLM SDKs directly in your code: import the OpenAI or Anthropic SDK in Python, call the API, process the response with pandas. There is no special "AI node" because you do not need one when you have full language access.

Windmill also added AI code generation that writes scripts from natural language descriptions, producing code with proper error handling and typed parameters. For teams building sophisticated AI workflows with custom models or complex prompt chains, Windmill's code-first approach gives the most flexibility. For drag-and-drop AI pipelines, n8n is the clear leader.

Pricing for open-source tools splits into two categories: the cloud-hosted plans and the real cost of self-hosting.

n8n Pricing

• Self-hosted Community: Free under the Sustainable Use License (fair-code). Unlimited workflows and executions. No time limit.
• Self-hosted Enterprise: Custom pricing. Adds SSO/SAML, LDAP, source control sync, audit logging, and environment variables management.
• Cloud Starter: $20/month for 2,500 executions, 5 active workflows
• Cloud Pro: $50/month for 10,000 executions, 50 active workflows
• Cloud Enterprise: Custom pricing

n8n counts executions, not individual node runs. A 10-step workflow triggered once counts as one execution. This makes n8n's cloud pricing dramatically cheaper per operation than Zapier or Make at scale. For more on how n8n stacks up against those tools, check our n8n vs Make vs Zapier breakdown.

Activepieces Pricing

• Self-hosted Community: Free under MIT license. This is the most permissive license of the three. No usage limits, no feature gating.
• Self-hosted Platform: $249/month. Adds custom branding, audit logs, custom roles, and project management features.
• Cloud Free: 1,000 tasks/month
• Cloud Pro: $15/month for 10,000 tasks
• Cloud Platform: $99/month for 50,000 tasks

Activepieces counts tasks similarly to Zapier: each action in a flow consumes one task. A 5-step flow uses 5 tasks per run. The MIT license is a genuine differentiator for companies that want to embed automation into their own products. Unlike n8n's fair-code license, MIT places no restrictions on commercial redistribution.

Windmill Pricing

• Self-hosted Community: Free under AGPLv3. Unlimited users and executions. Some features (audit logs, SAML) are gated.
• Self-hosted Enterprise: Starts at $200/month. Adds SSO/SAML, audit logs, Git sync, priority support, and dedicated Slack channel.
• Cloud Free: 1,000 executions/month, limited to 1 user
• Cloud Team: $10/user/month for 10,000 executions
• Cloud Enterprise: Custom pricing

Windmill counts executions like n8n (per workflow run, not per step). The per-user pricing model is unique among the three and works well for small teams but scales linearly with headcount. The AGPLv3 license requires that if you modify Windmill and offer it as a service, you must open-source your changes. This is more restrictive than MIT but less restrictive than n8n's Sustainable Use License for most deployment scenarios.

Real Self-Hosting Costs

The "free" self-hosted tier is never truly free. Budget for a VPS ($20 to $100/month), Postgres hosting, SSL, monitoring, backups, and engineering hours for upgrades. Expect $50 to $150/month in total infrastructure costs regardless of platform. That is still 3x to 10x cheaper than cloud plans at scale.

The number of built-in integrations determines how quickly you can connect your tools without writing custom code.

n8n: 400+ Nodes

n8n ships over 400 nodes covering CRMs, databases, messaging platforms, cloud services, and most popular SaaS tools. The community node system lets you install npm packages directly as new nodes, and custom node development follows a well-documented TypeScript SDK.

Activepieces: 200+ Pieces

Activepieces has a smaller but rapidly growing library, adding roughly 80 new pieces in 2025 alone. Coverage focuses on Google Workspace, Microsoft 365, Slack, Stripe, Shopify, Notion, and Airtable. A "Custom API" piece works with any REST endpoint, though it lacks the flexibility of n8n's HTTP Request node (which supports OAuth flows, pagination, and binary data natively).

Windmill: 100+ Hub Scripts + Any SDK

Windmill approaches integrations differently. Instead of dedicated nodes, the Windmill Hub offers pre-built scripts for common integrations. Because you are writing real code, you can import any npm package, Python pip package, or Go module directly. Need to connect to an obscure API? Import its SDK and write the integration. This makes Windmill's effective integration count unlimited for developer teams, but it means non-developers cannot add new integrations without code.

Windmill also supports resource types: typed connection objects for external services. Define a Postgres or OpenAI resource once, and any script can reference it by name. This is cleaner than n8n's credential system for teams managing many connections.

We ran synthetic benchmarks on all three platforms using identical hardware: a 4-core, 8 GB RAM VM running Ubuntu 22.04 with Postgres 15. Each test executed a workflow that fetched data from an HTTP endpoint, transformed it with a code step, and wrote results to Postgres.

Throughput (executions per minute)

• n8n (queue mode, 2 workers): 420 executions/min
• Activepieces: 280 executions/min
• Windmill (2 workers): 1,100 executions/min

Windmill's Rust scheduler significantly outperforms the Node.js-based engines. The gap widens with CPU-intensive workloads. For a Python script running pandas transformations on a 10,000-row dataset, Windmill completed the execution in 1.8 seconds versus n8n's 4.2 seconds (via the Execute Command node) and Activepieces' 5.1 seconds (via a code piece). Windmill's native Python worker avoids the serialization overhead that Node.js-based platforms incur when shelling out to Python.

Memory Consumption

• n8n (idle): 180 MB
• Activepieces (idle): 350 MB
• Windmill (full stack, idle): 520 MB

Windmill uses more memory at idle because of the multiple language runtime workers. Under load, n8n's memory scales linearly with concurrent executions. Activepieces' sandboxed model creates sharper memory spikes per execution but recovers faster. Windmill's memory usage stays the flattest because the Rust core adds negligible overhead per job.

Horizontal Scaling

n8n supports horizontal scaling through queue mode: add more worker containers behind a Redis queue. Windmill was designed for horizontal scaling from day one, with workers registering themselves to the central scheduler. Activepieces supports multiple workers in its self-hosted enterprise tier but the documentation for scaling is less mature than the other two.

For most small-to-mid-sized teams running fewer than 10,000 executions per day, all three platforms perform well on a single server. Windmill's performance advantage becomes relevant at scale or when individual executions involve heavy computation.

Enterprise readiness goes beyond performance. Audit trails, access controls, and compliance features determine whether a platform can pass procurement review.

n8n Enterprise

n8n's enterprise tier includes SAML/SSO, LDAP, role-based access control, execution logging with audit trails, environment variables for staging/production separation, source control integration (Git push/pull), and workflow sharing with granular permissions. The admin dashboard shows execution history, error rates, and resource usage. n8n also supports credential sharing with scoped access, so a team can use a shared Stripe API key without individual members seeing the secret.

Activepieces Enterprise

Activepieces' Platform tier adds custom branding (white-label the entire UI), custom roles, project-based organization, audit logging, and a management API for programmatic control. The white-labeling feature is unique among the three and targets agencies or SaaS companies that want to embed automation into their own products. SAML/SSO is available but was only added in early 2026, so it is less battle-tested than n8n's implementation.

Windmill Enterprise

Windmill's enterprise features include SAML/SSO, Git sync (bidirectional with GitHub/GitLab), audit logs, approval flows (require human approval before a script executes), dedicated worker groups (route sensitive workloads to isolated infrastructure), and priority queues. Windmill also supports deploy-to-prod workflows where changes go through staging, review, and promotion stages. For teams building an agentic workflow platform, Windmill's approval flows and worker isolation provide the governance layer that production AI agents require.

Windmill's Git integration is the strongest of the three. Every script and flow is stored as a file that syncs bidirectionally with your repository. You can edit scripts in your IDE, push to Git, and Windmill picks up the change automatically. This fits naturally into existing CI/CD pipelines and code review processes.

Open-source projects live or die by their communities. Here is where each platform stands in mid-2026.

n8n

• GitHub stars: 52,000+
• Monthly active contributors: ~120
• Funding: $50M+ total raised (Series B in 2024)
• Community forum: Active, with thousands of workflow templates shared

n8n is the safest bet for longevity. Significant funding, a large team, and a growing enterprise base make abandonment unlikely. The risk is license changes: n8n moved from Apache 2.0 to a Sustainable Use License in 2022, and another tightening is possible.

Activepieces

• GitHub stars: 14,000+
• Monthly active contributors: ~45
• Funding: Seed round ($3.2M) with Y Combinator backing

Activepieces is the youngest and carries more risk. The MIT license and YC backing provide credibility, but the team is smaller and the product less mature. The main risk is a pivot toward closed-core as the company seeks revenue growth.

Windmill

• GitHub stars: 12,000+
• Monthly active contributors: ~30
• Funding: Seed round (undisclosed), based in France

Windmill has the smallest community but the most technically sophisticated user base. The AGPLv3 license ensures the code stays open. The Rust foundation gives Windmill a technical moat that would be expensive for competitors to replicate. The main risk is limited brand awareness outside the developer community.

After deploying all three in production environments, here are our recommendations.

Choose n8n if:

• Your team is a mix of developers and non-technical users who both need to build workflows
• You want the largest ecosystem of pre-built integrations and community templates
• AI/LLM workflows are a core use case and you want visual, drag-and-drop AI pipeline building
• You need proven enterprise features (SSO, RBAC, audit logs) today, not on a roadmap
• Community size and long-term project viability are top priorities

Choose Activepieces if:

• Your users are primarily non-technical and you need the simplest possible interface
• You want to embed or white-label workflow automation inside your own SaaS product
• MIT licensing matters for your use case (commercial redistribution, no copyleft restrictions)
• You are migrating from Zapier and want the closest self-hosted equivalent in terms of UX
• Your workflows are straightforward (linear flows, moderate complexity) rather than deeply nested

Choose Windmill if:

• Your team is primarily developers who prefer writing Python, TypeScript, or Go over dragging nodes
• Performance matters: you are processing thousands of jobs per minute or running CPU-intensive scripts
• You need polyglot support (Python data scripts alongside TypeScript API integrations)
• Git-native development workflows (code review, CI/CD, staging environments) are non-negotiable
• You are building internal tools alongside automations and want a unified platform for both

There is no single winner. An operations team automating CRM and marketing processes will thrive with Activepieces. A startup building AI-powered internal tools will get more from n8n. A data engineering team processing millions of events will outperform on Windmill.

If you are not sure where your workflows fall, we can help you evaluate. Book a free strategy call and we will walk through your automation requirements, recommend the right platform, and help you ship a production deployment in weeks instead of months.