Dify vs n8n: An AI App Platform or an Automation Platform

Both Dify and n8n let you build AI agents without owning a Python runtime, and both can be self-hosted. They arrive from opposite directions, and that decides which one fits.

• Dify is an AI application platform. It builds agents, agentic workflows, chatbots, and RAG over your own data, publishable as a web app or an API, and is source-available rather than OSI open source.
• n8n is a workflow automation platform that grew strong AI capability. Its unit of work is a workflow connecting services, with LangChain nodes and local model support layered in.

The question underneath: is the AI the product, or is the AI one step in a process that connects other systems?

Quick comparison

When Dify is the right shape

Dify's premise is that the thing you're building is an AI application. Its documentation covers building agents and agentic workflows, chatbots, and integrating custom data for RAG, with the result publishable as a web app or consumed through an API.

That matters because it changes what's included by default. When your goal is "an assistant that answers questions over our documentation," you need ingestion, chunking, retrieval, a chat interface, and somewhere to host it. On an automation platform each of those is something you assemble; on Dify they're the product.

Choose Dify when:

• The deliverable is a chatbot, assistant, or AI app that people or systems will use directly
• RAG over your own documents is central rather than incidental
• You want a web app and an API without building either
• You're iterating on prompts and retrieval quality as the main work

When n8n is the right shape

n8n's premise is that AI is a step inside something larger. The unit of work is a workflow that fires on a trigger, moves data between services, and calls a model somewhere in the middle.

That's the right shape more often than people expect. "When a support ticket arrives, classify it, look up the customer, draft a reply, and route it" is 80% integration and 20% AI — and the integration is where the work actually is. n8n's node library is far broader across ordinary SaaS than Dify's.

Two other n8n properties worth knowing:

Billing is per workflow execution, not per step, so a long workflow costs the same as a short one. Complexity is free; frequency is not.

The licence is not open source, and n8n doesn't claim it is. n8n is source-available under the Sustainable Use License, which permits internal use, modification, and self-hosting, and forbids offering n8n to third parties as a hosted service. For internal automation that's a distinction without a practical difference; for an agency reselling managed instances it isn't.

Choose n8n when:

• The AI step sits inside a process touching several other systems
• You need breadth of SaaS integrations
• Triggers, scheduling, and error handling matter as much as the model
• You're already automating and want to add AI rather than start over

Self-hosting, both ways

Both offer it, and it's a real differentiator against most of the category.

Dify self-hosts through its Community Edition via Docker Compose, with Dify Cloud as the managed option including a free sandbox tier.

n8n self-hosts, with one detail worth stating precisely because it's widely assumed away: only the free Community Edition is unmetered. Per n8n's pricing, the Business plan can be self-hosted and is still priced by workflow executions, and self-hosted Enterprise uses custom execution pricing — a licence key reports production executions back to n8n. Self-hosting removes usage billing only on Community Edition.

The honest caveat applies to both: self-hosting trades a subscription for an operational responsibility — upgrades, backups, certificates, monitoring, and someone to page when it stalls. If nobody owns that, the managed option is the real comparison. Our guide to workflow orchestration tools covers where that burden becomes worth carrying.

Using both

More common than you'd guess, and the split is clean:

• Dify builds and hosts the AI application — the assistant, the retrieval pipeline, the API endpoint
• n8n handles the plumbing around it — triggers, integrations, routing results into other systems, scheduling

n8n calls Dify's API as one node in a larger workflow. You get Dify's AI-native tooling and n8n's integration breadth, at the cost of two systems to operate.

The step before either

Both platforms remove the need to write and host a runtime, which is genuinely most of the work. They still assume you'll design the thing — pick the retrieval strategy, write the prompts, wire the nodes.

The other common situation is that the useful thing already exists. Someone shared an agent, a workflow, a Claude Code configuration that does exactly your job, and the blocker is running it rather than designing it. Rebuilding it on a platform means throwing away a working version to guess at a replacement.

Taku is built for that case: an AI-native desktop workspace where you mirror an AI app or workflow that already works, run it on your own machine, and remix it into your own. It isn't a platform for building AI apps from scratch and doesn't compete with these — it's the option for when copying beats building. Taku is in Beta, and the Mac app is available now, and the free app library shows what mirroring looks like.

Deciding

1. Is the AI the product? → Dify.
2. Is the AI one step in a process? → n8n.
3. RAG over your documents is the core? → Dify.
4. Need many SaaS integrations? → n8n.
5. Data can't leave your infrastructure? → either; both self-host.
6. Reselling managed instances to clients? → check n8n's licence carefully; Dify is worth evaluating separately for that use.
7. Both problems are real? → run both, with n8n calling Dify's API.

If you want the wider field, AI agent builders covers the category and agentic workflows covers the shape of the work.

FAQ

Is Dify open source?

Not in the OSI sense. Dify is source-available under a modified Apache 2.0 licence: you may self-host and modify it for your own use, but operating a multi-tenant environment requires written authorisation, and you may not remove the Dify logo or copyright from the console when using its frontend.

Neither condition is waived for internal deployments. The licence treats a workspace as a tenant, and Dify's pricing lists the Community edition as a single workspace with multiple workspaces under Enterprise — so an internal deployment running several workspaces, or one that rebrands the console, still has to account for those terms. Single-workspace internal use that keeps the branding is the clean case; anything past that, read the licence first.

Is n8n open source?

No, and it doesn't claim to be. It's source-available under the Sustainable Use License — free for internal use, modification, and self-hosting, not for reselling as a hosted service. Dify sits in the same category under a modified Apache 2.0 licence, so neither is OSI open source despite both being commonly described that way.

Which is better for a chatbot over company documents?

Dify. Ingestion, retrieval, a chat interface, and hosting are the product rather than things you assemble.

Which is better for automating a business process?

n8n, comfortably. Its integration breadth and trigger handling are what the job needs.

Can they work together?

Yes — n8n calling Dify's API is a common pattern, giving you AI-native tooling plus integration breadth.

Do I need to self-host either?

No. Both offer managed cloud. Self-host when data residency requires it or when volume makes it cheaper, and only if someone will own the server — and note that self-hosting n8n only avoids execution billing on the free Community Edition.