Dify vs n8n: Which One for AI Workflows
In short
n8n and Dify both give you a canvas, boxes and arrows, and both can be self-hosted for free — but they are answering different questions. n8n is an automation platform: triggers, integrations, branching, retries, and an LLM node somewhere in the middle. Dify is an LLM application platform: prompts, RAG over your own documents, agents with tools, and a chat UI plus API you can ship to users. Pick n8n when AI is one step inside a business process. Pick Dify when the AI is the product. Operationally they are not equivalent: n8n self-hosts as a single container with SQLite by default, while Dify's Compose stack starts 7 core services plus 8 dependencies. Neither is OSI open source — n8n uses the Sustainable Use License (internal business use only), Dify uses a modified Apache 2.0 that forbids running it as a multi-tenant service and forbids removing its branding. All prices below verified as of July 2026.
Two different ideas of what a workflow is
In n8n, a workflow is a run. Something fires — a webhook, a schedule, a new row — data flows through nodes, each node transforms it or calls an API, and the run ends. Execution history is the primary artefact: you open a past run and inspect the JSON at every step. The mental model is a pipeline, and it comes from the Zapier lineage.
In Dify, a workflow is a program a user talks to. You build an app — chatbot, agent, or a workflow triggered by input — and Dify gives it a hosted chat interface, an API endpoint, conversation memory, a knowledge base and per-app logging. The mental model is a backend service whose logic happens to be a graph.
That difference shows up everywhere. n8n has no first-class concept of a document collection you can query; you bolt one on with a vector-store node and manage chunking yourself. Dify has no first-class concept of "when a Stripe payment succeeds"; its triggers are thin next to a platform with a node for everything.
Both projects are large and actively maintained — as of late July 2026, n8n sits around 198k GitHub stars and Dify around 150k, both with commits landing daily. Neither is a bet on an abandoned project.
Feature comparison
| n8n | Dify | |
|---|---|---|
| Core purpose | Workflow automation across services | Building LLM apps: chat, agents, RAG |
| Integrations | 1,000+ native nodes (n8n's own claim) | Curated: model providers, vector stores, ~50+ agent tools |
| Triggers | Webhooks, schedules, app events, polling | App input, chat message, API call, scheduled/event triggers |
| RAG over your docs | DIY: vector-store nodes, you own chunking and retrieval | Built in: upload, index, retrieval settings, reranking |
| Prompt work | Text in a node parameter | Prompt IDE with side-by-side model comparison |
| Agents | Agent node, sub-workflows as tools | Agent apps with function calling / ReAct and tool registry |
| Ships a user-facing app | No — you build the frontend | Yes — hosted chat UI plus API out of the box |
| Arbitrary code | JavaScript/Python code nodes, close to unrestricted | Sandboxed code node, deliberately narrower |
| Observability | Per-run step-by-step JSON, execution search | Per-app conversation logs, annotations, LLMOps view |
| Self-host footprint | One container (SQLite default) | 7 core services + 8 dependencies |
Read the "integrations" row carefully. The 1,000+ figure comes from n8n's own comparison page, which also frames Dify as a "constrained" environment. Vendor comparison pages are marketing; the honest version is that n8n's node library is genuinely much wider, and that this only matters if your workflow touches many systems. If your workflow is "user asks question → search our docs → answer", the node count is irrelevant and Dify's RAG pipeline saves you a week.
Self-hosting both
n8n is the lighter install by a wide margin. One docker run, SQLite on a volume, and you have a working instance; swap in Postgres when you care about the data, add Redis and queue mode when one process stops keeping up. n8n's own documentation is blunt about the trade-off: self-hosting "requires technical knowledge" and is "recommended for expert users", and the project ships a new minor version most weeks, so you are signing up for a real upgrade cadence.
Dify's Docker Compose deployment is a stack, not a container: api, api_websocket, worker, worker_beat, web, plugin_daemon and agent_backend as core services, plus Postgres, Redis, a vector database (Weaviate by default), an SSRF proxy, Nginx and friends. Documented minimum is 2 CPU cores and 4 GiB RAM; on macOS the docs ask for 2 vCPUs and 8 GiB in Docker Desktop. Upgrades need you to diff .env.example against your .env each release.
Practical read: on a small VPS you can run n8n alongside other things. Dify wants a box mostly to itself, and its footprint is closer to a small platform than to an app. If you are planning to put both on one machine, the sizing exercise in self-hosting an AI stack on one server applies directly.
Cost at small and medium scale
Self-hosted, both are free of licence cost and you pay for the server plus your own model API keys. That last part dominates: token spend on a busy RAG app will exceed the hosting bill quickly, and neither platform changes what OpenAI or Anthropic charge you.
Managed pricing, verified July 2026:
| Tier | n8n (pricing) | Dify (pricing) |
|---|---|---|
| Free | Community edition, self-hosted only | Sandbox: 200 message credits, 1 workspace, 5 apps, 50 MB knowledge |
| Entry paid | Starter €20/mo billed annually — 2,500 executions, 2,300 AI credits | Professional $590/year — 5,000 message credits/mo, 3 members, 50 apps, 5 GB |
| Mid | Pro €50/mo billed annually — 10,000 executions | Team $1,590/year — 10,000 message credits/mo, 50 members, 200 apps, 20 GB |
| Self-hosted paid | Business €667/mo billed annually — adds SSO, environments, Git version control | Enterprise — contact sales |
Two things worth knowing before you budget. n8n counts an execution as one whole workflow run regardless of how many nodes it contains, which is materially cheaper than per-step pricing. And the n8n self-hosted Community edition is missing a specific list of features that only a licence key unlocks: SSO, Projects, Git version control, environments, external secrets, external binary storage, log streaming and workflow/credential sharing. Queue mode is included. If a self-hosted team needs shared workflows and SSO, the free tier ends abruptly at €667/mo — check the edition comparison in n8n's hosting docs before you standardise on it.
Licences: neither is as open as it looks
Both projects are described as open source in most write-ups. Both have conditions that matter if you build a product on top.
n8n's Sustainable Use License permits use "only for your own internal business purposes or for non-commercial or personal use", and redistribution only free of charge for non-commercial purposes. Files with .ee. in the name are excluded entirely and require an enterprise licence. Running client automations internally is fine; selling an n8n-powered automation service to customers is the case to get legal advice on.
Dify's licence is Apache 2.0 plus two conditions: you may not use the source to operate a multi-tenant environment without written authorisation, and you may not remove or modify the Dify logo and copyright in the console or apps unless you avoid its frontend entirely. So a white-labelled, multi-customer AI SaaS built on Dify needs a commercial licence.
If you need a genuinely permissive licence for an automation layer you resell, Activepieces is MIT and worth a look instead.
Which to pick for which job
- Ops and integration glue — "invoice paid → provision account → notify Slack → append to sheet", with an LLM classifying something in the middle: n8n.
- A product feature made of an LLM — support assistant over your docs, an agent your users chat with, anything needing conversation memory and a shippable API: Dify.
- RAG you don't want to hand-build — ingestion, chunking, reranking, citations: Dify, comfortably.
- Hundreds of SaaS connections and event triggers: n8n, not close.
- Smallest server you can get away with: n8n.
- Both is a legitimate answer and a common one: Dify owns the reasoning and exposes an endpoint, n8n calls it and handles everything around it. That is one HTTP node, and it avoids forcing either tool to do the thing it is bad at.
Card-level notes with install commands live in the arsenal for Dify and n8n.
FAQ
Is Dify better than n8n for AI workflows?
For AI applications — chatbots, agents, RAG over your own documents — yes: Dify gives you prompt management, a knowledge base, conversation memory and a hosted UI you would otherwise build. For AI steps inside business automation, n8n wins because the surrounding 90% of the workflow is integrations, triggers and error handling, which is what it was built for.
Can I self-host Dify and n8n for free?
Yes, both. n8n Community edition runs indefinitely without a licence key but omits SSO, Projects, Git version control, sharing and a few scaling features. Dify's Community Edition is free and single-workspace. In both cases you still pay for the server and for your own LLM API usage.
Is n8n really open source?
Not by the OSI definition. n8n uses the Sustainable Use License: free for internal business, personal and non-commercial use, with enterprise-only source files carved out. It is usually called "fair-code". For most teams this changes nothing; for anyone reselling n8n as a service it changes everything.
Do I need both Dify and n8n?
Often, and it is not a cop-out. Expose your Dify app as an API, call it from an n8n workflow, and each tool does what it is good at. The cost is one more service to host and monitor, so only do it once a single tool is visibly straining.
What are the main Dify alternatives?
Flowise for a lighter LangChain-flavoured builder, LangGraph or a plain framework if you would rather own the code, and n8n itself if your AI logic is simple enough to live in a couple of nodes. If the blocker is Dify's multi-tenant restriction rather than its features, the licence is the thing to solve first — not the tool.