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: a clean drag-blocks UI, tight JS-frontend embed, and a community-driven roadmap. 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 is expensive: shipping an end-user-facing AI product on raw Flowise is weeks of UI work; 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. Tight UI, community-driven, with a clean fit into JavaScript-shaped stacks.
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
- Winner for "flow inside a JS product": Flowise. Lighter footprint, native Node fit.
- Winner for "ship a standalone AI product": Dify. Full surface ships by default.
- Winner for JavaScript-rooted teams: Flowise. LangChain.js engine and Node host.
- 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 | Open source (custom) |
| Maintainer | FlowiseAI + community | 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 are open source. Both have paid hosted tiers. Model inference dominates.
| Cost line | Flowise | Dify |
|---|---|---|
| Platform licence | Free (Apache 2.0) | 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) |
| Typical chat flow (per 1k Q&A) | ~$5-30 on GPT-4o-mini | ~$5-30 on GPT-4o-mini |
| Hidden costs | Build product surface around it | Self-host ops (DB, vector, upgrades) |
The pattern: identical per-token economics. Dify costs more operationally because the platform is wider; Flowise costs more in product-build time because you supply the surface.
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?
- Dropping a flow into a JS app: Flowise wins. Native Node fit, clean embed widget, lighter ops.
- Shipping an end-to-end AI product for the team: Dify wins. Knowledge base, workspace, and full product surface ship by default.
- Greenfield prototype: Dify gets you to a product faster; Flowise gives you more flexibility once you decide to ship into your own surface.
Meta-recommendation: a lot of teams pick 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
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FAQ
- Flowise vs Dify -- which one should I pick?
- If you want a visual LLM canvas in the Node.js ecosystem that embeds cleanly into JS frontends, pick Flowise. If you want a full AI product platform with workspace, chat UI, knowledge base, and model gateway out of the box, pick Dify. Flowise is a canvas; Dify is a 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?
- Identical per-token economics; both call the same models. The cost split is operational: Dify is heavier to self-host (app + DB + vector); Flowise is lighter (a single Node service plus your store). For chat-shaped flows the token bill is essentially the same.
- Can Flowise replace Dify in production?
- For embedding a flow into a JS product -- yes. For shipping a standalone AI product with knowledge base, workspace, and full product surface -- no. Dify ships the surface; Flowise expects you to build it.
- Are Flowise and Dify open source?
- Yes -- both. Flowise is Apache 2.0 with a separate hosted offering. Dify is open source under a custom licence: free to self-host for most use cases, with restrictions on reselling it as multi-tenant SaaS.
- 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?
- Uncommon -- they overlap on the canvas layer. Most teams pick one. The cleaner composition is Flowise as the flow engine inside a JS product, or Dify as the full AI product platform with no Flowise.