If you want an assistant that can hold a conversation and actually complete tasks, a single tool usually stalls in the same place.
Dify is good at understanding, but ask it to query your company database, open a ticket, or chain three external systems together, and it gets increasingly strained. n8n is good at executing, but you cannot have users talk to it in natural language.
Connect the two and each does what it is good at.
The 30-second version
| Dify | n8n | |
|---|---|---|
| Responsible for | Understanding intent, managing knowledge | Executing actions, connecting systems |
| Good at | Natural language, RAG | API integration, retries, scheduling |
| Price | NT$1,599/mo | avg. NT$499/mo (billed yearly) |
| How to decide | If the answer is written in a document, use Dify; if it has to be looked up right now, you need n8n | |
Where the line falls
| Dify | n8n | |
|---|---|---|
| Responsible for | Understanding intent, managing knowledge, composing replies | Executing actions, connecting systems, handling failures |
| Good at | Natural language, conversation state, RAG retrieval | API integration, scheduling, retries, error handling |
| Not good at | Complex multi-system integration and fault tolerance | Understanding human language, maintaining a knowledge base |
| Plan | NT$1,599/mo | avg. NT$499/mo (billed yearly) |
How they are typically connected
The two talk over a webhook, and the flow looks like this:
- The user asks "when is that order from last month arriving?"
- Dify recognizes this as an order lookup intent and extracts the parameters it needs from the conversation
- Dify calls an n8n webhook and passes the parameters across
- n8n queries the order system and the logistics API, retrying where necessary
- n8n returns a structured result
- Dify turns the result into a sentence a human would say
Step four is the crux. A lookup may span two or three systems, the other side's API may time out, a retry may be needed — that is n8n's everyday work, and doing it inside Dify is a struggle.
Why not let one of them do everything
The problem with giving it all to Dify: it does have workflow orchestration, and simple integrations are fine. But when you need retries against a third-party API, scheduled execution, or a different path on failure — this is territory that automation tools have worked on for years. Forcing it is painful, and it is hard to investigate when something breaks.
The problem with giving it all to n8n: n8n can call language models too, and one-off classification or summarization is no trouble at all. But it has no knowledge base management, no conversation state and no ready-made chat interface. You have to keep track of "what this user just said" yourself, and that is far more work than it sounds.
When you only need one of them
Let's be honest. Dify at NT$1,599/mo plus n8n at avg. NT$499/mo (billed yearly) comes to about NT$2,098/mo on average, and not every requirement is worth that.
- Only n8n: your requirement is rule-based — receive a keyword, look something up, reply in a fixed format. Nobody wants to "converse" with it, so Dify is redundant.
- Only Dify: what you want is a support bot that answers questions, with every answer already in your documents and no live lookups against external systems. In that case Dify on its own is complete.
- Both: users ask in natural language, and the answer has to be fetched live from another system.
The test is simple: if the answer is "written in a document", Dify is enough; if the answer "has to be looked up right now", you need n8n.
Build the small version first
Do not design the full architecture up front. Pick one question that gets asked most often and get that path working end to end — Dify understands, calls n8n, gets the result, replies.
Once that path works, adding a second kind of question is just one more branch. And if the first path does not work, you will be glad you did not build the whole thing first.
One concrete flow
- The user asks "when is that order from last month arriving?"
- Dify determines this is an order lookup intent and extracts the parameters from the conversation
- Dify calls an n8n webhook and passes the parameters across
- n8n queries the order system and the logistics API, retrying where necessary
- n8n returns a structured result
- Dify turns the result into a sentence a human would say
Step four is the crux. A lookup may span two or three systems, the other side's API may time out, a retry may be needed — that is n8n's everyday work, and doing it inside Dify is a struggle.
Why not let one of them do everything
The problem with giving it all to Dify
It does have workflow orchestration, and simple integrations are fine. But when you need retries against a third-party API, scheduled execution, or a different path on failure — this is territory that automation tools have worked on for years. Forcing it is painful, and it is hard to investigate when something breaks.
The problem with giving it all to n8n
n8n can call language models too, and one-off classification or summarization is no trouble at all. But it has no knowledge base management, no conversation state and no ready-made chat interface. You have to keep track of "what this user just said" yourself, and that is far more work than it sounds.
Look at the cost together
The two together average about NT$2,098/mo, plus model usage. That is not a trivial number, so make sure you really need both:
| Requirement | How many you need |
|---|---|
| Reply with fixed content on a keyword | n8n only |
| Answer questions from documents | Dify only |
| Understand the question, then look it up in another system | Both |
FAQ
Q: How do I connect the two?
Through a webhook. Dify works out the intent, extracts the parameters and calls an n8n webhook; n8n runs and returns a structured result; Dify turns it into human language. The setup is covered in the webhook guide.
Q: Can I buy just one to start with?
Yes, and that is what we recommend. Work out which half is your real bottleneck first — "it does not understand people" or "it cannot find the data".
Q: Can Dify not call APIs itself?
It can, and simple integrations are fine with its HTTP node. You need n8n when there are many integrations, when you need retries and error handling, or when you need scheduling.
Q: Will latency be high?
It adds a step, because there is one more network round trip. In practice the bottleneck is still the model's response time rather than the hand-off between the two.
Q: Where is the best place to start?
Pick one question that gets asked most often and get that path working. Once it works, adding a second kind of question is just one more branch; if the first path does not work, you will be glad you did not build the whole thing first.
Sources and further reading
Dify's features and interface change between versions, so check the official documentation before you start:
- Dify official documentation
- Dify release notes — the authority on feature changes
- Dify source code and issue tracker
Further reading
- Dify tutorial: build your first AI chatbot
- n8n webhooks and third-party integrations
- Where to host a LINE bot: the webhook cannot go down
- What Dify is technically good at: RAG knowledge bases, workflow orchestration, and why it is resource-hungry
- RoamerHost managed Dify plans
- RoamerHost managed n8n plans
Want someone to build it for you?
If you would rather not build these workflows yourself, or the scale is large enough that you want someone planning alongside you, Roamer Tech (RoamerHost's parent company) takes on contract work in business process automation and AI agents:
