Comparison · Updated 2026-06-21
Flowise vs Dify
Two open-source projects, both visual, both compared by teams shopping for a low-code LLM platform. Flowise is a Node.js-rooted visual LLM canvas built on LangChain.js; its official core-team maintenance ended on August 31, 2026 and its repository is archived. Dify is a full self-hostable AI product platform: the canvas is one feature alongside a chat UI, knowledge base, team workspace, model gateway, and embed widget. They overlap on "drag blocks to build a flow" but live in different product categories. Picking the wrong one creates avoidable implementation work: shipping an end-user-facing AI product on raw Flowise requires building more of the product surface; embedding a single flow inside a JS service via Dify means adopting a whole platform layer you do not need.
Flowise
Node.js-rooted visual LLM canvas built on LangChain.js. Existing users need a maintained fork or migration plan now that official core-team maintenance has ended.
See alternatives →Dify
Self-hostable AI product platform. Workflow canvas, RAG pipeline, model gateway, chat UI, and a team workspace shipped together -- batteries-included for AI apps.
See alternatives →The short answer
- Existing JS deployment boundary: Flowise fits Node.js stacks, but continued use needs a maintained fork or migration plan.
- Winner for "ship a standalone AI product": Dify. Full surface ships by default.
- New deployment boundary: Dify has an active maintained product surface; do not select Flowise without an ownership plan.
- Winner for non-engineer product iteration: Dify.
- Best for: Flowise as a canvas inside a JS service; Dify as the whole AI product.
Snapshot comparison
Before the section-by-section breakdown, the one-screen version.
| Dimension | Flowise | Dify |
|---|---|---|
| Primary shape | Visual LLM canvas | Full AI product platform |
| Runtime | Node.js | Python (Docker stack) |
| Audience | Web devs, JS-first builders | Product, ops, builders |
| License | Apache 2.0 outside specified enterprise paths; commercial terms for specified enterprise code | Dify Open Source License, based on Apache 2.0 with additional conditions |
| Maintenance | Community or internally maintained forks; official core-team maintenance ended | LangGenius |
| Engine | LangChain.js | Custom workflow engine |
| Built-in chat UI | Embed widget + playground | Hosted chat + embed widget + app surface |
| Built-in knowledge base | Components on canvas | First-class product feature |
| Workspace / multi-user | Basic | First-class team workspaces |
| Model gateway | Per-flow config | Platform-wide gateway |
| Embedding in JS frontends | Native fit | Embed widget |
| Self-host ops weight | Lighter (single Node service) | Heavier (app + DB + vector) |
| Best for | Flow inside a JS product | Standalone AI product |
Two different mental models
The right tool depends on which of these reads like your problem.
Flowise thinks "visual canvas for LLM flows in the Node ecosystem". You drag blocks for prompts, retrievers, models, and tools, wire them together, and either embed the flow in a JS product or expose it via API. The canvas is the primary product; the embed widget is the simplest way to ship.
Dify thinks "AI product platform". You log into a self-hosted dashboard, drag blocks onto a canvas, attach documents to a knowledge base, pick a model, and publish a chat app. The platform is the product.
If your problem is "drop a chatbot into our Next.js app this week", that is Flowise shaped. If your problem is "ship an internal product the support team uses every day", that is Dify shaped.
Use cases -- when each one wins
Flowise fits when
- Next.js / Remix / SvelteKit products. JS-shaped apps adding an LLM step.
- Web-team owned AI. Frontend engineers who do not want Python services.
- Quick chat embeds. The embed widget is genuinely clean.
- Smaller, curated component set. Less to learn for non-LangChain veterans.
- Lighter self-host footprint. A single Node service plus your store.
Dify fits when
- Internal RAG chatbots. Upload PDFs and policies, get a working Q&A bot.
- Customer-facing chat apps with full surface. Not just a widget, a managed app.
- Non-engineer prompt iteration. Product or ops staff edit prompts and flows.
- Model gateway needs across many apps. One place for keys and usage.
- AI prototypes that should look like products. Time-to-demo matters.
Learning curve
Flowise is friendlier for JS engineers. The runtime is Node, the engine is LangChain.js, the embed is a JS snippet. Most web devs ship a working chat widget in an afternoon.
Dify is friendlier as an end-to-end product. A non-engineer can ship a working internal bot in an afternoon: log in, drag blocks, attach a knowledge base, publish. The cost is adopting the platform layer even when you only wanted a single flow.
Practical rule: if the artifact is "a flow we drop into a JS app", Flowise wins. If the artifact is "an AI product the team uses every day", Dify wins.
Pricing comparison
Both projects publish self-hostable source under different terms. Flowise official core-team maintenance ended on August 31, 2026 and its repository is archived; evaluate a maintained fork or migration path before adopting it for new production work.
| Cost line | Flowise | Dify |
|---|---|---|
| Platform licence | Apache 2.0 core; designated Enterprise components use a commercial licence | Free self-host (custom licence) |
| Self-hosting | Lighter (single Node service) | Heavier (app + DB + vector) |
| Model inference | Pay-per-token | Pay-per-token |
| Hosted runtime | Flowise Cloud | Dify Cloud |
| Vector store | BYO (LangChain.js stores) | Bundled (Weaviate or external) |
| Workload cost | Depends on calls, context, retrieval, hosting, and maintenance | Depends on calls, context, retrieval, hosting, and plan |
| Hidden costs | Build product surface around it | Self-host ops (DB, vector, upgrades) |
The pattern: compare model calls, retrieval, hosting, and platform usage. Dify packages a wider application surface; Flowise requires an explicit plan for ongoing maintenance or migration now that official core-team maintenance has ended.
Final verdict
These two overlap on the canvas layer but live in different categories. The right call comes down to two questions: do you need a flow inside a JS product, or a standalone AI product?
- Existing maintained Flowise deployment: Flowise remains a native Node fit with a clean embed widget and lighter ops, provided your team or fork owns maintenance.
- Shipping an end-to-end AI product for the team: Dify wins. Knowledge base, workspace, and full product surface ship by default.
- Greenfield prototype in 2026: Dify is the safer maintained choice. Select Flowise only when its Node fit outweighs the lifecycle risk and you have an explicit fork-ownership or migration plan.
Decision rule: choose a canvas tool because it "looks like a platform" and then spend months building the surface around it -- Dify ships that surface by default. Plenty of teams adopt Dify when all they actually wanted was a flow they could call from one JS service, and the platform layer becomes ops debt. The wider landscape is in the AI Agent Frameworks pillar; the deeper shortlists are best Flowise alternatives and best Dify alternatives.
Related guides
Related alternatives
Next read
FAQ
- Flowise vs Dify -- which one should I pick?
- Dify is the safer default for a new maintained deployment. Flowise remains relevant to existing Node.js deployments and maintained forks, but official core-team maintenance ended on August 31, 2026 and the repository is archived. Flowise is a canvas; Dify is a broader product platform.
- Is Flowise a product platform like Dify?
- Not exactly. Flowise has a clean chat embed and an API for flows, but the product layer is narrower than Dify. Dify ships workspace, knowledge base, multi-app management, and a model gateway in the same dashboard. Flowise is sharper as "a flow inside a JS product".
- Is Dify easier to learn than Flowise?
- For end-to-end product iteration -- yes. Dify is a dashboard for the whole AI product. For embedding a flow inside a Next.js or Node backend, Flowise is lighter and feels more native to JS engineers.
- How do token costs compare?
- Both can call the same models, but total cost depends on call count, context, retrieval, hosting, and platform usage. Flowise reached end of life on August 31, 2026 after its repository was archived, so fork ownership or migration belongs in the cost model.
- Can Flowise replace Dify in production?
- Technically it can serve embedded flows, but a new production adoption now requires a maintained fork or an explicit migration plan because official core-team maintenance ended on August 31, 2026 and the repository is archived. Dify also packages a broader product surface.
- Are Flowise and Dify open source?
- Both publish self-hostable source under different terms. Flowise applies Apache 2.0 outside specified enterprise paths and commercial terms to designated Enterprise code. Dify uses the Dify Open Source License, based on Apache 2.0 with additional conditions relevant to multi-tenant services and frontend branding. Review the current licences for the intended use.
- Which one wins for RAG workloads?
- Dify -- end-to-end. Document upload, chunking, retrieval, chat UI, and a knowledge base UI are day-one features. Flowise has RAG components on the canvas but expects you to build the product around them.
- Can I use Flowise and Dify together?
- They overlap on the canvas layer, so using both adds two workflow surfaces and two operating boundaries. If both are retained, document which one owns orchestration and which one owns the product surface.