Comparison · Updated 2026-06-18
Dify vs Langflow
Two open-source projects, both self-hostable, both compared constantly -- and they compete for the same engineer's attention without solving quite the same problem. Dify is a full AI product platform: workflow canvas, RAG pipeline, model gateway, chat UI, and a team workspace shipped together. Langflow is a visual canvas on top of LangChain where nodes map directly to LangChain components and the output is exportable Python. Picking the wrong one is expensive: a "ship an internal chatbot tomorrow" job built on Langflow is weeks of surrounding UI work; a deeply custom LangChain pipeline forced through Dify's canvas hits ceilings the moment you need a node Dify does not ship. This is the honest side-by-side.
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 →Langflow
Python-based visual AI application builder with generic core components, provider bundles, custom components, and API serving.
See alternatives →The short answer
- Winner for a standalone AI product with RAG and chat UI: Dify. The product surface is part of the platform.
- Winner for a flow embedded in an existing service: Langflow. The canvas can remain one part of a team-owned application.
- Winner for learning curve: Dify -- non-engineers ship apps in an afternoon.
- Winner for code portability: Langflow -- the graph is just LangChain underneath.
- Best for: Dify for product/ops-owned AI applications; Langflow for engineering teams already invested in LangChain.
Snapshot comparison
Before the section-by-section breakdown, the one-screen version.
| Dimension | Dify | Langflow |
|---|---|---|
| Primary shape | Self-hosted product platform | Visual canvas for LangChain pipelines |
| Audience | Product, ops, builders | Python-oriented builders and engineers |
| License | Open source (custom licence) | MIT |
| Maintainer | LangGenius | Langflow project team |
| Model coverage | Built-in model gateway, many providers | Core model component plus installed provider bundles |
| Built-in RAG | Upload + chunk + retrieve out of the box | Wire loader + vectorstore yourself |
| Built-in chat UI | Hosted chat + embed widget | Playground only; ship UI yourself |
| Code export | API endpoint only | Exports to Python / LangChain code |
| Workspace / multi-user | First-class team workspaces | Single-tenant by default |
| Component library | Dify-defined blocks | Every LangChain component is a node |
| Learning curve | Drag, attach docs, publish | Friendly if you know LangChain |
| Hosted option | Dify Cloud (paid tiers) | Langflow Cloud (paid tiers) |
| Best for | RAG chatbots, internal AI products | Prototyping LangChain pipelines visually |
Two different mental models
The right tool depends on which of these reads like your problem.
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 mental model is closer to Retool or n8n than to a Python library. The platform is the product; the workflow canvas is one feature among many.
Langflow thinks "visual layer for LangChain". You drop LangChain primitives onto a canvas, wire them together, and either run them in the playground or export the graph to Python. The mental model is "LangChain code, but with a diagram". The canvas is a productivity layer; teams can call the resulting flow from an existing service instead of adopting a complete product platform.
If your problem is "ship an internal RAG chatbot the support team can edit", that is Dify shaped. If your problem is "prototype a LangChain pipeline visually, then deploy it as a Python service alongside our other code", that is Langflow shaped.
Use cases -- when each one wins
Dify fits when
- Internal RAG chatbots. Upload PDFs and policies, get a working Q&A bot for support, HR, or sales enablement.
- Customer-facing chat apps. Embed a chat widget on a website without writing the UI.
- Non-engineer prompt iteration. Product or ops staff edit prompts and flows in a canvas without shipping code.
- Model gateway needs. One place to manage API keys, swap providers, and track usage across many apps.
- Team workspaces. Multiple AI apps in one self-hosted instance with separate ownership.
Langflow fits when
- Prototyping LangChain pipelines. Wire chains, agents, and retrievers visually, then export to Python.
- Teaching LangChain to teammates. The canvas makes chain composition tangible for new joiners.
- Embedded LLM workflows. When the flow is one piece of a team-owned service, not the entire product.
- Using every LangChain component. Tools, retrievers, parsers, memory -- if LangChain has it, Langflow exposes it.
- Hybrid code + canvas workflows. Engineers iterate on prompts in the canvas; CI tests the exported Python.
Learning curve
Dify is friendlier during initial setup. Its Docker deployment, visual blocks, knowledge-base workflow, and built-in chat surface reduce the implementation burden for a non-engineer. The mental model is "build an app on a canvas" -- familiar to anyone who has used Zapier, n8n, or Retool.
Langflow is designed for Python-oriented builders. Its visual editor composes generic core components, installed provider bundles, and custom components. The API-serving and Python extension paths can fit an engineering-owned workflow; non-engineers should validate the maintenance surface before committing because the abstractions are LangChain abstractions.
Practical rule: if the people building the AI experience are product, ops, or solo builders, Dify clicks instantly. If they are Python engineers already invested in LangChain, Langflow clicks instantly. Pick by team shape and existing framework allegiance, not by feature checklist. If the deliverable is a flow inside an existing service, lean Langflow; if it is a standalone product surface, lean Dify.
Pricing comparison
Both projects are open source and free to self-host. The real bill is model inference and, optionally, a hosted tier.
| Cost line | Dify | Langflow |
|---|---|---|
| Platform licence | Free self-host (custom licence) | Free (MIT) |
| Self-hosting infrastructure | Docker stack (app + DB + vector store) | Python service + your own vector store |
| Model inference | Pay-per-token (any provider via gateway) | Pay-per-token (any LangChain provider) |
| Hosted runtime | Dify Cloud: tiered subscription | Langflow Cloud: tiered subscription |
| Observability | Built-in usage + logs | Logs + LangSmith if you wire it |
| Vector store | Bundled (Weaviate or external) | BYO (Pinecone, Weaviate, pgvector) |
| Hidden costs | Self-host ops (DB, vector, upgrades) | Building the surrounding product |
The pattern: Dify and Langflow use different licences and package infrastructure differently. Database, vector-store, app-server, model, and hosted-plan costs all matter; their relative weight depends on the workload. Langflow adds less ops overhead but offloads "build the product around the graph" to you.
Final verdict
These two are not exactly substitutes -- they overlap on "visual canvas for AI flows" but live in different product categories. The right call comes down to two questions: do you want a finished AI product platform, or a visual layer for code you already write?
- Want a finished AI product platform with RAG and chat UI: Dify wins. Knowledge base, chat UI, and model gateway are day-one features instead of weeks of plumbing.
- Already on LangChain and want a visual layer: Langflow wins. Nodes mirror LangChain, graphs export to Python, the editor sits next to your code.
- Neither feels right: the workflow may belong in a code-first agent framework (LangGraph, OpenAI Agents SDK) instead of a canvas. See the AI Agent Frameworks pillar for the wider landscape, or the best Dify alternatives and best Langflow alternatives shortlists.
Meta-recommendation: a lot of "we need a visual AI builder" decisions are actually "we need a finished AI product" decisions -- and Dify ships faster than Langflow plus custom UI work. A lot of "we need an AI platform" decisions are engineering teams who already write LangChain and would be slowed down by a product platform's opinions -- and Langflow is a friendlier home there. Match the tool to the team's existing surface.
Next reads
FAQ
- Dify vs Langflow -- which one should I pick?
- If you want a self-hostable AI product platform with RAG, model gateway, chat UI, and team workspace shipped together, evaluate Dify. If you want a Python-based visual builder with core components, provider bundles, custom components, and API serving, evaluate Langflow. Dify packages more product surfaces; Langflow exposes a builder framework.
- Is Dify just a fancier Langflow?
- No. Dify is a product platform: workspaces, applications, knowledge bases, model gateway, chat UI. Langflow is a visual builder for LangChain pipelines that exports to code. Dify ships a chatbot product end-to-end. Langflow ships a graph you then deploy elsewhere. Different scope.
- Is Langflow easier to learn than Dify?
- The learning curve depends on the operator. Python developers may prefer Langflow's component model and extension path. Product or operations teams may prefer Dify's packaged application surfaces. Test each with the people who will maintain it rather than assuming one maps directly to an existing framework.
- How do token costs compare?
- Both can call the same underlying models, but total cost depends on call count, context, retrieval, hosted plans, and self-hosted infrastructure. Dify and Langflow package those operating components differently, so neither has a universal cost advantage.
- Can Langflow replace Dify in production?
- Only if you build the surrounding product yourself: chat UI, knowledge base management, user accounts, model gateway. Langflow gives you the graph; you ship the app around it. Dify ships the app and lets you edit the graph inside it. For "we want a usable AI chatbot tomorrow", Dify wins. For "we want a custom Python service with a visual editor", Langflow wins.
- Are Dify and Langflow open source?
- Both publish source and support self-hosting, but the terms differ. Langflow is MIT-licensed. Dify uses the Dify Open Source License, based on Apache 2.0 with additional conditions relevant to multi-tenant services and frontend branding. Review each current licence for the intended deployment.
- Which one wins for RAG workloads?
- Dify includes document upload, chunking, retrieval, and a chat UI. Langflow can implement RAG, but you wire the loader, embedder, and vector store, then expose the graph to your own UI. For internal Q&A bots, Dify requires less initial assembly; for RAG as one step inside a larger LangChain pipeline, Langflow offers lower-level graph composition.
- Can I use Dify and Langflow together?
- They overlap in scope, and adding both means two visual canvases, two databases, and two upgrade paths. If both are required, define one as the backend and document the HTTP boundary, ownership, and failure path.