Dify vs n8n
One assembles an AI app around a model. The other automates a process and can call a model along the way. Which one describes what you are actually building?
The short answer
Choose Dify when the deliverable is an AI app people chat with, and n8n when it is an automated business process that happens to include an AI step.
Dify is built around one job. Get a large language model in front of a user, grounded in your documents, with prompt versioning and a chat window included. n8n does something different: connect systems and move data between them on a trigger. It ships over 500 app integrations, plus a code node for anything the built-in ones miss.
Both can technically do a version of the other's job. Dify has a workflow canvas now. n8n has AI Agent and LangChain-backed nodes. That overlap is why the question gets asked, and it is also why the answer is not close once you name the actual deliverable.
Side by side
Drawn from both projects' own documentation and our own production use of n8n, checked in August 2026.
| Dimension | Dify | n8n |
|---|---|---|
| Built around | An LLM app: chat UI, prompt orchestration, retrieval | A workflow: triggers, app connectors, data movement |
| Retrieval-augmented generation | Built in: document ingestion, chunking, a knowledge base | Bring your own vector store and wire it as steps |
| Chat interface | Ships with one, embeddable in a page or a widget | None. n8n has no user-facing surface |
| App connectors | Handful, aimed at model providers and data sources | Over 500, aimed at business systems |
| Prompt management | Versioned prompts, A/B testing, first-class | A text field inside whichever node calls the model |
| Where custom logic lives | A workflow canvas, added after the app-builder core | A code node running JavaScript or Python, native to the tool |
| Typical deliverable | A support bot or internal Q&A app end users open | A pipeline that runs unattended on a trigger or a schedule |
| Self-hosting | Open source, Docker Compose, or a hosted cloud plan | Open source, Docker or npm, or a hosted cloud plan |
Dify
Strengths
- RAG is a first-class feature, not a chain you assemble yourself.
- Prompt versioning and A/B testing without extra tooling.
- A chat UI ships with the app, so there is nothing separate to build.
- Fast to get a working LLM app in front of real users.
Trade-offs
- Thin on the business-system integrations a process automation needs.
- The workflow canvas is newer and less battle-tested than n8n's.
- Built-in retrieval is convenient but harder to swap for a different vector store later.
- Not the tool for a process with no chat surface at all.
n8n
Strengths
- Over 500 connectors to real business systems, not just model providers.
- A code node for anything a built-in node cannot do.
- Runs unattended on triggers and schedules, which is most process automation.
- Self-hosting avoids the cloud tier's per-execution pricing.
Trade-offs
- No chat interface. An AI app still needs one built separately.
- RAG is not built in. You wire the vector store and the chunking yourself.
- Prompt text lives inside a node with no versioning around it.
- The visual canvas gets harder to read past a few dozen nodes.
- A user asks a questionDeliverable is an app someone opens and types into
- Dify: knowledge base retrievalBuilt-in chunking and search over your documents
- Dify: chat UI respondsShips with the app, nothing separate to build
- A record changes in another systemDeliverable is a process nobody has to open
- n8n: trigger firesWebhook, schedule, or a polled app event
- n8n: model call is one nodeFollowed by whichever system gets updated next
The fork is at the top: who or what starts the run, and whether the output is a conversation or a completed task.
How to choose
Ask what the person on the other end is doing. If they are typing a question and reading an answer, that is an app. If nobody is watching and the system just needs to finish a task, that is a process.
- 01Choose Dify if the deliverable is a chatbot or an internal Q&A tool over your documents.
- 02Choose n8n if the deliverable is a pipeline that moves data between systems on a trigger.
- 03Choose n8n if the AI step is one part of a longer process touching a CRM, a spreadsheet, or a ticketing system.
- 04Choose neither if the task is a single prompt run once. That is a script, not a platform.
01Can Dify and n8n work together?
Yes, and it is a common pairing. n8n calls Dify's API as one step in a longer workflow. Dify handles the retrieval and the chat logic. n8n handles everything around it: the trigger, the CRM update, the follow-up email. Neither tool has to do the other's job.
02Is n8n's AI Agent node a Dify replacement?
Not for an app with a knowledge base. n8n's AI Agent node calls a model with tools and memory, which covers a lot of agent work. But it has no built-in document ingestion or chunking. You would build retrieval yourself with separate nodes.
03Which one is cheaper to run?
Both are open source and self-hostable, so the honest comparison is your own infrastructure cost, not a list price. n8n's cloud tier bills per workflow execution. That adds up fast on high-volume automations, and it is one reason we run it self-hosted in production.
04Which one scales better for a production system?
n8n has the longer track record running high-volume, unattended pipelines, which is the shape most production automation takes. Dify's core strength, a grounded chat app, scales differently: the bottleneck is usually retrieval quality, not execution volume.
