These three tools get compared constantly, because on the surface they do the same thing: build an LLM app from a visual interface, with little or no code.
Use them for a while, though, and you find their design goals are very far apart. And the cost of choosing wrong usually does not show up right away — it surfaces months later, when you want to move your data out, change your retrieval logic, or sell the feature to your own customers.
The 30-second overview
| Dify | Coze | Flowise | |
|---|---|---|---|
| Positioning | Enterprise-oriented LLM app platform | Bot platform for general users | Developer-oriented visual orchestration |
| Who builds it | Open source, self-hostable | ByteDance, mainly SaaS | Open source, self-hostable |
| Where data lives | You decide | Their servers | You decide |
| RAG depth | High, tunable from the UI | Medium, few details exposed | High, but you assemble it |
| Learning curve | Medium | Low | High |
| Self-hosting difficulty | High (many containers) | N/A | Low (a single app) |
| Best for | Teams building a company knowledge base | Individuals who want to try things fast | Engineers who want full control |
Dify: the complete, enterprise-oriented platform
Core capabilities
It ships with a full RAG knowledge base pipeline — document upload, chunking strategy, vectorization, hybrid retrieval, reranking — all adjustable from the interface. That is what makes it friendliest to non-engineers: you can build a bot that answers from company documents without knowing what an embedding is.
Where your data lives
You can self-host and keep the data in your own environment. There is an official cloud version too, but you have the choice — and that is the key difference from a pure SaaS.
Strengths and concerns
The strength is completeness: knowledge base, workflow orchestration, agents and multi-model switching all live in one system, so you are not stitching pieces together.
The concern is that it is heavy. A multi-container architecture means a high bar for self-hosting, and once a knowledge base grows, retrieval quality does not improve on its own — it needs tuning. There is a full write-up of that in what happens as your knowledge base grows.
Coze: fastest to start, at the cost of control
Core capabilities
From signup to a working conversational bot can take a bit over ten minutes. It hides all the complicated parts, which is a plus if you just want to try something out.
Where your data lives
On their servers. Your knowledge base documents and your conversation logs are all up there.
If what you plan to feed it is product documentation, customer data or internal policies, that needs to clear your compliance team or your manager first — and you need to ask before you start, not after you finish.
Strengths and concerns
The strength is speed and zero operations. The concern is that features and pricing are decided by someone else: you cannot fork it, you cannot self-host it, and your freedom to move data out is limited.
To be fair: for the “I want to validate an idea quickly” case, most of these concerns do not apply. The problem only starts once it becomes part of a real product.
Flowise: building blocks for developers
Core capabilities
LangChain sits underneath, so you can assemble any retrieval strategy you like. The flexibility is the highest of the three, and so is the ceiling.
Where your data lives
Self-hostable, entirely your call.
Strengths and concerns
The strengths are freedom and light weight — a single Node.js app is enough to get running, a much lower bar than Dify.
The concern is that it will not decide anything for you. You pick the chunk size, you design the retrieval strategy, and when results are poor it is on you to work out which stage broke. For engineers that is a feature; for a non-engineering team it is a barrier.
Three differences that actually drive the decision
| Difference | Why it matters |
|---|---|
| Data residency | Hard to change later. If you plan to feed it internal documents, this alone narrows the field |
| Licensing model | Open source means the vendor cannot change the rules unilaterally. Critical when you are building it into your own product |
| Who makes the decisions | Dify decides most parameters for you, Flowise hands all of them to you, Coze sits in between |
Picking by scenario
An internal company knowledge base, operated by non-engineers
Dify. RAG parameters are tunable from the interface, so you can keep optimizing without writing code.
Building an AI feature into a product you sell
Dify (self-hosted or managed). Handing the lifeline of your product to a commercial platform you do not control is a risk — not because the vendor will act in bad faith, but because pricing and feature decisions are not yours to make.
You are an engineer and want full control of the retrieval logic
Flowise. This is where LangChain’s flexibility genuinely pays off.
You just want to spin up a bot quickly, with no sensitive data
Coze. Demanding data sovereignty in this scenario is over-engineering.
Your data is subject to compliance rules and cannot leave the country
Dify or Flowise, self-hosted or on a local managed service.
Running Dify on RoamerHost
If your conclusion is Dify but you would rather not deal with that whole set of containers: RoamerHost offers fully managed Dify, live about 60 seconds after payment, with 2 vCPU / 10 GB storage at NT$1,599/mo, including SSL and a dedicated subdomain, with your data inside your own isolated container.
One thing that is often misunderstood is worth repeating: the monthly fee does not include language model costs. You bring your own API key and pay the provider directly for model calls. Details are in the pre-purchase FAQ.
FAQ
Q: Are all three free?
Dify and Flowise are open source, so self-hosting costs no license fee, but you pay for the server and the operations. Coze has a free tier, and beyond it you pay their pricing. None of the three includes language model call costs — those are billed separately.
Q: Can I start on Coze and move to Dify later?
You can, but be prepared: you will rebuild the knowledge base and redo the flows. The data structures differ on each side, and there is no ready-made export/import.
If you already know you will end up self-hosting, using Coze to validate the idea and Dify for the real version is reasonable. But if you pick Coze to save effort, the rework may cost more than starting on Dify would have.
Q: Is self-hosting Dify really that hard?
It is a multi-container architecture (API, worker, frontend, PostgreSQL, Redis, a vector database), and the official recommendation starts at 2 cores and 4 GB. Getting it up with Docker Compose is not hard; what is hard is the upgrades, backups and monitoring afterwards. There is a full cost breakdown in self-hosted vs managed.
Q: My RAG results are poor — did I pick the wrong tool?
Usually not. The bottleneck in retrieval quality is normally document quality and chunking strategy, not the platform. The same set of documents will not produce wildly different results across the three — what differs is how many parameters you can tune, and whether you know which ones to tune.
Sources and further reading
The capabilities and positioning described here follow each project’s official documentation. Actual features change between versions, so confirm directly before deciding:
- Dify website and Dify documentation
- Dify source code (GitHub) — the authority on license terms and self-hosting requirements
- Flowise website and Flowise source code
- LangChain — the framework underneath Flowise
- Coze website — pricing and features per their official announcements
Further reading
- Dify tutorial: build your first AI chatbot
- What is Dify: an introduction to the no-code AI app platform
- Dify self-hosted vs managed: a real one-month cost breakdown
- As your Dify knowledge base grows: trading off retrieval quality and resources
- Resource limits on managed Dify: when 2 vCPU is not enough
- Dify plus n8n: AI understands, n8n executes
- RoamerHost managed Dify plans
Want someone to build it for you?
If you would rather not assemble these flows yourself, or the scope 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 agent development:
