Dify vs Langflow -- which one should I pick?

If you want a full self-hostable AI product platform with RAG, model gateway, chat UI, and team workspace shipped together, pick Dify. If you want a LangChain-native visual canvas where the nodes are actual LangChain components and the output is exportable Python, pick Langflow. Dify is a product. Langflow is a visual layer on top of a Python library.

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?

For LangChain developers, yes -- nodes map one-to-one to LangChain primitives they already know. For everyone else, Dify is friendlier because it hides the framework and shows a product canvas instead. Pick by audience: engineers comfortable with LangChain land faster on Langflow; product or ops staff land faster on Dify.

How do token costs compare?

Similar per call -- both call the same underlying models. Dify adds a model gateway and built-in vector store; Langflow expects you to attach your own vector store and runtime. Inference dominates either way. Platform overhead (DB, vector store, app server) is rounding error vs the model bill at any non-trivial usage.

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?

Yes -- both. Langflow is MIT-licensed and maintained by the Langflow team (now part of DataStax). Dify is open source under a custom licence: free to self-host for most cases, with restrictions on reselling it as multi-tenant SaaS. Read the Dify licence if you plan to run it as a managed service for paying customers.

Which one wins for RAG workloads?

Dify -- out of the box. Document upload, chunking, retrieval, and a chat UI are day-one features. Langflow can do RAG, but you wire the loader, embedder, and vector store yourself, then expose the graph to your own UI. For internal Q&A bots, Dify is faster to ship; for RAG as one step inside a larger LangChain pipeline, Langflow is more natural.

Can I use Dify and Langflow together?

Rarely worth it. They overlap in scope and adding both means two visual canvases, two databases, and two upgrade paths. Pick one. If you really need both, run Langflow as a backend that Dify calls via HTTP -- but most teams find that one canvas is enough.

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.

Published 2026-06-18 · ~6 min read · Independent, no paid placements (disclosure)

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

LangChain-native visual canvas. Nodes mirror LangChain primitives, graphs export to Python, and the editor lives next to your code -- a visual layer for LangChain pipelines.

See alternatives →

The short answer

Winner for AI products with RAG and chat UI: Dify. Document upload, retrieval, and a usable chat interface are day-one features.
Winner for LangChain teams who want a visual layer: Langflow. Nodes are LangChain primitives; graphs export to Python.
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	LangChain-aware engineers
License	Open source (custom licence)	MIT
Maintainer	LangGenius	Langflow team (DataStax)
Model coverage	Built-in model gateway, many providers	Any LangChain-supported provider
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; the runtime is still your Python service.

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.
Custom Python services. When the graph is one piece of a larger codebase, not a standalone app.
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 in the first afternoon. Spin up Docker, log in, drag blocks, attach a knowledge base, publish a chat app. A non-engineer can ship a working internal bot the same day. The mental model is "build an app on a canvas" -- familiar to anyone who has used Zapier, n8n, or Retool.

Langflow is friendliest if you already know LangChain. Every node mirrors a LangChain class you have probably used. Wiring is just composition. The export-to-Python button makes the graph feel like a real codebase artifact, not a proprietary diagram. Engineers ship a first graph in an hour; non-engineers struggle 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.

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: licence cost is zero for both. Model inference dominates. Dify adds platform infrastructure (database, vector store, app server) which is real ops work but rounding error vs the inference bill at any non-trivial usage. 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 full self-hostable AI product platform with RAG, model gateway, chat UI, and team workspace shipped together, pick Dify. If you want a LangChain-native visual canvas where the nodes are actual LangChain components and the output is exportable Python, pick Langflow. Dify is a product. Langflow is a visual layer on top of a Python library.
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?: For LangChain developers, yes -- nodes map one-to-one to LangChain primitives they already know. For everyone else, Dify is friendlier because it hides the framework and shows a product canvas instead. Pick by audience: engineers comfortable with LangChain land faster on Langflow; product or ops staff land faster on Dify.
How do token costs compare?: Similar per call -- both call the same underlying models. Dify adds a model gateway and built-in vector store; Langflow expects you to attach your own vector store and runtime. Inference dominates either way. Platform overhead (DB, vector store, app server) is rounding error vs the model bill at any non-trivial usage.
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?: Yes -- both. Langflow is MIT-licensed and maintained by the Langflow team (now part of DataStax). Dify is open source under a custom licence: free to self-host for most cases, with restrictions on reselling it as multi-tenant SaaS. Read the Dify licence if you plan to run it as a managed service for paying customers.
Which one wins for RAG workloads?: Dify -- out of the box. Document upload, chunking, retrieval, and a chat UI are day-one features. Langflow can do RAG, but you wire the loader, embedder, and vector store yourself, then expose the graph to your own UI. For internal Q&A bots, Dify is faster to ship; for RAG as one step inside a larger LangChain pipeline, Langflow is more natural.
Can I use Dify and Langflow together?: Rarely worth it. They overlap in scope and adding both means two visual canvases, two databases, and two upgrade paths. Pick one. If you really need both, run Langflow as a backend that Dify calls via HTTP -- but most teams find that one canvas is enough.

Best Dify alternatives → Best Langflow alternatives → AI Agent Frameworks pillar →