Comparison · Updated 2026-06-21
Langflow vs Dify
Two open-source projects, both visual, both used in production -- and they sit on different category lines. Langflow is a Python-rooted visual LLM canvas with deep LangChain DNA, backed by DataStax: you drag blocks, build a flow, and call it via API. Dify is a full self-hostable AI product platform: the canvas is just one feature alongside a chat UI, knowledge base, team workspace, model gateway, and embed widget. The closest mirror angle is our Dify vs Langflow page. Picking the wrong one is expensive: shipping an end-user-facing AI product on raw Langflow is weeks of UI work; embedding a single flow inside an existing service via Dify means adopting a whole platform you do not need.
Langflow
Python-rooted visual LLM canvas. Drag blocks for prompts, retrievers, agents, and models -- with deep LangChain DNA and DataStax backing.
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 "build a flow we embed elsewhere": Langflow. Lighter, no platform to adopt.
- Winner for "ship a standalone AI product": Dify. Chat UI, knowledge base, workspace ship by default.
- Winner for Python-rooted teams: Langflow. LangChain DNA and DataStax integrations.
- Winner for non-engineer product iteration: Dify. Full product surface, not just a canvas.
- Best for: Langflow as a canvas inside a service; Dify as the whole AI product.
Snapshot comparison
Before the section-by-section breakdown, the one-screen version.
| Dimension | Langflow | Dify |
|---|---|---|
| Primary shape | Visual LLM canvas | Full AI product platform |
| Audience | Python builders, data teams | Product, ops, builders |
| License | MIT | Open source (custom) |
| Maintainer | DataStax | LangGenius |
| Engine | LangChain (Python) | Custom workflow engine |
| Built-in chat UI | Basic playground | Hosted chat + embed widget |
| 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 |
| RAG primitives | Canvas blocks (LangChain) | End-to-end product layer |
| Self-host ops weight | Lighter | Heavier (DB + vector + app) |
| Hosted option | DataStax Langflow Cloud | Dify Cloud |
| Best for | Flow inside a service | Standalone AI product |
Two different mental models
The right tool depends on which of these reads like your problem.
Langflow thinks "visual canvas for LLM flows". You drag blocks for prompts, retrievers, models, and tools, wire them together, and either embed the flow in your existing service or expose it as an API. The canvas is the primary product.
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; the workflow canvas is one feature among many.
If your problem is "build a flow we will call from our Python backend", that is Langflow shaped. If your problem is "ship an internal chatbot over our policy docs that the support team can edit prompts in", that is Dify shaped.
Use cases -- when each one wins
Langflow fits when
- Embedded LLM workflows. Build a flow on the canvas, call it from an existing service.
- Python-shaped backends. Existing Django/FastAPI services adding an LLM step.
- Data-team owned AI. Analysts who already know LangChain Python.
- Astra DB / Cassandra integration. Tight DataStax backing.
- Lighter self-host footprint. When you do not want to adopt a full product platform.
Dify fits when
- Internal RAG chatbots. Upload PDFs and policies, get a working Q&A bot.
- Customer-facing chat apps. Embed a chat widget on a website without writing UI.
- Non-engineer prompt iteration. Product or ops staff edit prompts and flows.
- Model gateway needs. Manage API keys, swap providers, track usage across many apps.
- AI prototypes that should look like products. Time-to-demo matters.
Learning curve
Langflow rewards LangChain familiarity. If you already write LangChain Python, the canvas reads like the API you already use. The artifact is a flow you embed somewhere -- you supply the rest of the product.
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 -- DB, vector store, app -- even when you only wanted a single flow.
Practical rule: if the artifact is "a flow we wire into something bigger", Langflow 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 | Langflow | Dify |
|---|---|---|
| Platform licence | Free (MIT) | Free self-host (custom licence) |
| Self-hosting | Lighter (Python / Docker) | Heavier (app + DB + vector) |
| Model inference | Pay-per-token | Pay-per-token |
| Hosted runtime | DataStax Langflow Cloud | Dify Cloud |
| Vector store | BYO (Astra DB native) | 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. Langflow costs more in product-build time because you supply the surface around the canvas.
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 or a product, and how much platform are you willing to adopt?
- Embedding a single flow into an existing service: Langflow wins. Lighter footprint and no platform to adopt.
- Shipping an end-to-end AI product non-engineers will use: Dify wins. Chat UI, knowledge base, and workspace are day-one features.
- Greenfield prototype: Dify gets you to a working product faster; Langflow gives you more flexibility once you decide to ship into a custom 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. Conversely, plenty of teams adopt Dify when all they actually wanted was a flow they could call from one service, and the platform layer becomes ops debt. Match category, not just features. The wider landscape is in the AI Agent Frameworks pillar; the deeper shortlists are best Langflow alternatives and best Dify alternatives.
Related guides
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FAQ
- Langflow vs Dify -- which one should I pick?
- If you want a visual canvas focused on building LLM workflows you embed elsewhere, pick Langflow. If you want a full AI product platform with workspace, chat UI, knowledge base, and model gateway out of the box, pick Dify. Langflow is a canvas; Dify is a platform.
- Is Langflow a product platform like Dify?
- Not quite. Langflow gives you a visual canvas, an API to call your flow, and a hosted runtime via DataStax. Dify ships the canvas plus a full product layer: chat UI, knowledge base, team workspace, model gateway, embed widget. For "build a flow", they overlap; for "ship an AI product", Dify is the wider category.
- Is Dify easier to learn than Langflow?
- For shipping an end-user-facing app, yes -- Dify is a dashboard for entire AI products. For building a flow you embed into an existing service, Langflow is lighter because you do not adopt the platform piece. Match the tool to the artifact you actually need to ship.
- How do token costs compare?
- Both call the same underlying models. Cost is driven by flow shape, not platform. For equivalent flows, token use is essentially identical. Dify is slightly more expensive operationally because the platform layer adds DB and vector ops; Langflow is lighter to host.
- Can Langflow replace Dify in production?
- For embedding a flow into an existing product -- yes. For shipping a standalone AI product to non-engineers -- no. Dify ships the chat UI, knowledge base, and workspace; Langflow expects you to bring your own product layer.
- Are Langflow and Dify open source?
- Yes -- both. Langflow is MIT-licensed and maintained by DataStax. 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. Langflow has RAG primitives on the canvas but you build the product around it. For "internal Q&A bot", Dify wins; for "RAG step inside a larger flow we embed", Langflow wins.
- Can I use Langflow and Dify together?
- Less common -- they overlap on the canvas layer and most teams pick one. The cleaner composition is Langflow as the flow engine inside a service, with a custom front-end; or Dify as the full product platform with no Langflow.