Dify vs Open WebUI: Chat or App Builder?

You Don’t Need a Forklift to Chat With Gemma

Full example: Clone the working files at github.com/KingPin/sumguy-examples/tree/main/llm/dify-vs-open-webui

Open WebUI is a chat UI. Dify is an app builder. If you want to talk to your local models, upload a PDF, and let your family do the same, run Open WebUI and stop reading. If you want to build an application that happens to call an LLM (a support bot, a document pipeline, an agent with branching logic), run Dify.

People compare them because both give you a web page, a model dropdown, and a knowledge base upload button. Underneath, one is a car and the other is a forklift. Both move things. You don’t take the forklift to the grocery store, and you don’t move a couch with a sedan. Versions checked today (2026-10-16): Open WebUI v0.12.3 and Dify 1.17.1.

What Each One Is For

Open WebUI is a ChatGPT-style interface. You log in, pick a model, and type. It talks to Ollama and to any OpenAI-compatible endpoint (vLLM, LM Studio, OpenRouter, a LiteLLM proxy). RAG, web search, tools, functions, and pipelines are extras bolted onto the chat. The unit of work is a conversation. For the plugin side, see the tools, functions and pipelines post.

Dify is an LLMOps platform. The unit of work is an app. You draw a workflow on a canvas (LLM node, knowledge retrieval node, HTTP node, branch, loop), test it, then publish it as a web app or an HTTP API. The chat box is one possible output, not the product. The Dify deep dive covers building workflows. This post stays on the comparison.

The practical difference: in Open WebUI, a user asks a question. In Dify, a developer designs what happens when someone asks a question.

The Footprint Gap

This is where the forklift shows up. Open WebUI is one container:

docker run -d -p 3000:8080 \
  --add-host=host.docker.internal:host-gateway \
  -v open-webui:/app/backend/data \
  --name open-webui --restart always \
  ghcr.io/open-webui/open-webui:main

That is the command from the project README. The UI lands on port 3000, data lives in the open-webui volume. The image has tags: :main for the plain build, :cuda for NVIDIA GPU acceleration, and :ollama which bundles Ollama inside the same container. The default vector store is embedded Chroma, so RAG needs no second container.

Dify is a stack. The official route:

git clone https://github.com/langgenius/dify.git
cd dify/docker
cp .env.example .env
docker compose up -d

Then open http://localhost/install to create the admin account. The project’s stated minimum is 2 CPU cores and 4 GiB of RAM. In my reading of the compose file, the default .env.example sets COMPOSE_PROFILES=${VECTOR_STORE:-weaviate},${DB_TYPE:-postgresql},collaboration, which starts these services:

That is 16 services in the default profile set, one of them a one-shot. Open WebUI is 1. I did not measure idle RAM on either for this post, so I won’t invent numbers. The stated 4 GiB minimum for Dify versus a single Python container tells you the shape of it. Postgres, Redis, a vector database, and a plugin runtime all want memory before you ask a single question.

Want a lighter Dify? Change VECTOR_STORE in .env to another supported store, or pick one you already run. The compose file ships profiles for many (Qdrant, pgvector, Milvus, and others). You still get the api, worker, and plugin pieces.

Neither one runs models. Both need a backend like Ollama next to them.

Model Backends

Open WebUI points at a backend with an environment variable:

services:
  open-webui:
    image: ghcr.io/open-webui/open-webui:main
    ports:
      - "3000:8080"
    environment:
      - OLLAMA_BASE_URL=http://host.docker.internal:11434
      - OPENAI_API_BASE_URL=http://litellm:4000/v1
    extra_hosts:
      - "host.docker.internal:host-gateway"
    volumes:
      - open-webui:/app/backend/data

volumes:
  open-webui:

OLLAMA_BASE_URL and OPENAI_API_BASE_URL both exist in the current source, along with the plural OLLAMA_BASE_URLS and OPENAI_API_BASE_URLS forms for several backends at once, semicolon-separated. Pull a model on the Ollama side (ollama pull gemma4:12b, same tag as in the Gemma 4 post) and it shows up in the dropdown.

Dify handles models differently. They are configured in the web UI, not in .env. Model providers are plugins now, run by the plugin_daemon container. You install the Ollama or OpenAI-API-compatible provider plugin in Settings, then give it a base URL. That URL has to be reachable from inside the Dify containers, so localhost is wrong. Use your host’s LAN address. Once added, any workflow node can pick the model.

The upside of Dify’s approach: you swap models per node. A cheap local Qwen 3.6 for classification, a bigger model for the final answer. Open WebUI picks one model per chat (it can compare two side by side, but that is still chat).

RAG and Knowledge Bases

Open WebUI: attach files to a chat, or build a Knowledge collection and reference it with #. One container, embedded vector store, settings under the admin panel. It is quick and good enough for “chat with my notes.”

Dify: a Knowledge Base is a first-class object with chunking settings, an embedding model, retrieval mode, and a vector database behind it (that Weaviate container). You attach it to a Knowledge Retrieval node in a workflow and control exactly what context reaches the prompt. More knobs, and more ways to misconfigure.

My rule: if you want to ask questions about a folder of documents, Open WebUI wins on time to first answer. If you want a repeatable pipeline (retrieve, rerank, answer in a fixed JSON shape, call a webhook), Dify wins.

Agents, Tools, Workflows

Open WebUI tools are Python functions or OpenAPI tool servers the model can call during a chat. Functions and pipelines modify or replace the model pipeline itself. It is flexible, but the logic lives in code you write and paste into an editor.

Dify is built around its workflow canvas: branches, loops, parallel nodes, HTTP requests, code nodes that run in the sandbox container, and agent nodes that pick tools. You can hand a non-programmer a diagram and they can follow it. At 2 AM, when the workflow returned nonsense, you can open the run log and see which node produced it. That visibility is worth real money in debugging time.

If your need is “the model should call my home automation API when I ask,” Open WebUI tools are enough. If your need is “when a webhook arrives, summarize it, classify it, branch on the class, and post to three places,” you want Dify (or n8n with Ollama, a different tool for similar work).

Users, Auth, Roles

Open WebUI is built for people sharing a box. New signups default to the pending role until an admin approves them (DEFAULT_USER_ROLE). It has roles, user groups, and per-group permissions on models, knowledge, and tools, plus trusted-header SSO via the WEBUI_AUTH_TRUSTED_* variables. Per-user chat history is the default experience. For a family or a small team, this is what you want. See also Open WebUI vs LibreChat.

Dify’s auth model is about builders. You have a workspace with members and roles. The people who use your published apps are not necessarily Dify users at all. A published web app can be public, or you front it with your own auth. That is a feature if you ship apps and a gotcha if you expected a household chat login.

API Exposure

Open WebUI serves an OpenAI-style /api/chat/completions route and an /api/models route. Admins can turn on API keys with ENABLE_API_KEYS, which defaults to off. Dify does not expose an OpenAI-compatible endpoint. I checked the current service API controllers in the repo, and the routes are Dify’s own: /chat-messages, /completion-messages, /workflows/run, /conversations, and friends.

curl -X POST "http://localhost/v1/chat-messages" \
  -H "Authorization: Bearer $DIFY_APP_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "inputs": {},
    "query": "Summarize my backup policy",
    "response_mode": "blocking",
    "user": "home-lab-user"
  }'

Each published app gets its own key, and the app type decides the endpoint. A chatflow or chat app uses /chat-messages. A pure workflow app uses /workflows/run.

That has a direct consequence. You cannot paste a Dify app URL into Open WebUI’s OpenAI connection field and expect it to work. The request and response shapes differ.

Running Both

The setup I would run at home: Open WebUI is the front door for people, Dify holds the workflows that need real logic, and a small bridge connects them. Open WebUI’s Pipe functions are the bridge. A Pipe is a Python class that appears as a model in the dropdown, and its pipe method receives the chat body and returns text. The Pipe structure below follows the Open WebUI docs, and the Dify call follows the /chat-messages route above. I tested it against a stub server that returns Dify’s blocking response shape, not against a live Dify install, so test it before you trust it.

import requests
from pydantic import BaseModel, Field


class Pipe:
    class Valves(BaseModel):
        DIFY_URL: str = Field(default="http://nginx/v1")
        DIFY_APP_KEY: str = Field(default="")

    def __init__(self):
        self.valves = self.Valves()

    def pipes(self):
        return [{"id": "dify-support-bot", "name": "Dify: Support Bot"}]

    def pipe(self, body: dict) -> str:
        content = body["messages"][-1]["content"]
        # Messages with images arrive as a list of parts; keep the text ones.
        if isinstance(content, list):
            content = " ".join(p.get("text", "") for p in content if p.get("type") == "text")
        query = content
        r = requests.post(
            f"{self.valves.DIFY_URL}/chat-messages",
            headers={"Authorization": f"Bearer {self.valves.DIFY_APP_KEY}"},
            json={
                "inputs": {},
                "query": query,
                "response_mode": "blocking",
                "user": "open-webui",
            },
            timeout=120,
        )
        r.raise_for_status()
        return r.json()["answer"]

Paste it under Admin Panel, Functions, fill in the valves, and “Dify: Support Bot” shows up as a model. Limits: this sends only the last message and starts a fresh Dify conversation each time. Carry conversation_id across turns if you need memory. It also needs both stacks on a shared Docker network, or the Dify URL set to a reachable address.

Compose for that shared network:

services:
  open-webui:
    networks:
      - default
      - docker_default

networks:
  docker_default:
    external: true

Dify’s .env.example sets no COMPOSE_PROJECT_NAME, so Compose names the project after the folder (dify/docker) and the network becomes docker_default. On that network the nginx service answers as http://nginx, which is why the Pipe defaults to http://nginx/v1. Confirm the name with docker network ls if you renamed anything.

A community Dify pipe may already exist for your case. Search the Open WebUI community site before writing your own.

Licenses (Read Them, Both Are Unusual)

Neither project is plain Apache or MIT. I fetched both LICENSE files today.

Dify is “a modified version of the Apache License 2.0” with two extra conditions. First, you may not use the source to operate a multi-tenant environment without written authorization, where one tenant equals one workspace. Second, you may not remove or modify the logo or copyright information in the Dify console or applications, which applies to the frontend (the web/ directory or the web image). Using the backend without the frontend is outside that clause. Commercial use as a backend or app platform is allowed. Contributors also agree the producer can change the terms.

Open WebUI uses its own “Open WebUI License”, a BSD-3-style text with a fourth clause. You may not alter, remove, or replace Open WebUI branding unless one of three exceptions applies: the deployment has 50 or fewer end users in any rolling 30 days, you have written permission, or you hold an enterprise license. Contributions fall under a CLA. Older code stays under its prior licenses, tracked in LICENSE_HISTORY.

For a home lab, both are fine as-is. The traps arrive when you resell access to many customers (Dify’s multi-tenant clause) or white-label for a larger audience (both branding clauses). Neither is OSI open source by the usual definition, so check before you build a business on a rebrand. I am not a lawyer.

Side by Side

So Which One?

1. Chatting with local models, sharing with family: Open WebUI.
2. One-off “chat with my docs”: Open WebUI.
3. Building an app or pipeline others call over HTTP: Dify.
4. Branching logic you want to see and debug on a canvas: Dify.
5. Both needs, one front door: Open WebUI plus a Pipe into Dify.

If you’re torn, start with Open WebUI. It costs one container. You will know within a week whether you keep wishing for a workflow canvas. If you do, add Dify beside it. Your 2 AM self will thank you for not starting with sixteen containers.

Common Questions

Can Open WebUI run Dify workflows?

Yes, through a Pipe function. Dify has no OpenAI-compatible endpoint, so Open WebUI cannot add it as a normal connection. A Pipe function calls the Dify /chat-messages or /workflows/run API with an app key and returns the answer as a model in the dropdown.

How much RAM does Dify need compared to Open WebUI?

Dify’s README states a minimum of 2 CPU cores and 4 GiB of RAM for the default Docker stack, which runs about 16 services. Open WebUI runs as a single container and states no minimum in its README. Model inference needs separate memory on whatever runs Ollama.

Can I use Dify for free in a company or a small business?

Yes for internal use and for building your own apps, since Dify allows commercial use under its modified Apache 2.0 license. You need a commercial license to run a multi-tenant service, where each customer gets a workspace, and you may not remove the logo or copyright in the web frontend.

Can I remove the Open WebUI branding?

Only in three cases. The deployment must have 50 or fewer end users in any rolling 30 days, or you need written permission from the copyright holder, or you need an enterprise license. Otherwise the Open WebUI license treats branding removal as a material breach.

Do Dify and Open WebUI both work with Ollama?

Yes. Open WebUI connects through the OLLAMA_BASE_URL environment variable. Dify connects through its Ollama model provider plugin, configured in the web UI with a base URL that the Dify containers can reach, so use the host’s LAN address rather than localhost.

Related Reading

• Dify: Visual Agent Workflows
• Open WebUI vs LibreChat: Self-Hosted ChatGPT Alternatives Compared
• Open WebUI Tools, Functions and Pipelines Explained
• Flowise vs Langflow: Build AI Pipelines Without Writing a Novel
• n8n + LLM: Building Automations That Actually Think