Ollama vs Open WebUI

Stop comparing them and start stacking them

The reason "Ollama vs Open WebUI" gets asked a hundred times a week is that both projects show up on every "self-hosted ChatGPT" list, and the naming doesn't make their relationship obvious. Let's fix that in one sentence.

Ollama runs the model. Open WebUI is what you look at while it runs.

That's it. Ollama is a runtime — you tell it "pull llama3.1", it pulls the weights, exposes an API on port 11434, and serves inference. It gives you a CLI and an HTTP endpoint. It does not give you a browser chat interface.

Open WebUI is a browser chat interface. It talks to any OpenAI-compatible API. Ollama exposes an OpenAI-compatible API. They fit together like a plug and a socket.

When people actually ask "which one"

There are three real questions hiding in the "Ollama vs Open WebUI" search box:

1. "I only want to install one thing. Which?"

Then install Open WebUI with its bundled Ollama option. The official Docker image can spin up Ollama alongside itself: docker run -d --name open-webui -p 3000:8080 -v open-webui:/app/backend/data ghcr.io/open-webui/open-webui:ollama. You get both in one container. Not the setup for production, perfectly fine for "I just want to try this tonight."

2. "I want a chat UI but I don't want to run models locally."

Install only Open WebUI, then point it at OpenAI, Groq, Anthropic, or any other OpenAI-compatible provider. You skip Ollama entirely. This is the fastest "self-hosted UI, cloud model" setup.

3. "I want local models but I don't want a UI."

Install only Ollama. Use its CLI, or hit its API from your own tooling. Skip Open WebUI. This is the developer / scripting path.

The real trade-offs (if you're picking one over the other)

Only-Ollama path

Pros: One process, minimal RAM overhead beyond the model itself, MIT license, tiny attack surface, official installers for macOS/Linux/Windows.

Cons: No chat history persistence, no multi-user, no RAG, no tool calling in a friendly UI. If someone else in your household wants to use it, they're either learning the CLI or you're building your own UI.

Only-Open-WebUI path

Pros: Full ChatGPT-shape experience — conversations, multi-user with auth, RAG over uploaded docs, tool calls, model switching, prompts library, image generation via SD/DALL-E connectors. BSD-3-Clause license.

Cons: Needs a model source. Without Ollama or a cloud API key, it's a blank room with no lights. Also: significantly more moving parts — Postgres or SQLite, embeddings, background workers.

Combined (the actual recommended setup)

Two Docker containers, one docker-compose file:

services:
  ollama:
    image: ollama/ollama
    volumes: [ollama:/root/.ollama]
    ports: ["11434:11434"]
  open-webui:
    image: ghcr.io/open-webui/open-webui:main
    environment:
      - OLLAMA_BASE_URL=http://ollama:11434
    volumes: [open-webui:/app/backend/data]
    ports: ["3000:8080"]
    depends_on: [ollama]

volumes:
  ollama:
  open-webui:

Ten lines. Ten minutes to bring up. This is the setup 90% of self-hosters end up on.

Hardware, honestly

• 7B models (Llama 3.1 8B, Mistral 7B): 8GB RAM minimum, GPU optional. Runs on Apple Silicon, decent modern laptops, a $6/mo VPS is too slow to be pleasant.
• 13B models: 16GB RAM, GPU strongly recommended. CPU inference is 2-5 tok/s territory.
• 70B models: Serious GPU or Apple Silicon with 64GB+ unified memory. This isn't a $6/mo VPS project.

Open WebUI itself is negligible — a few hundred MB and one CPU core. Ollama plus the model is the resource hog.

License comparison, briefly

• Ollama — MIT. Do anything.
• Open WebUI — BSD-3-Clause. Do anything, include attribution.

Both are safe for commercial and internal use with essentially zero legal thinking.

Community signals

Both projects have huge, active communities. Ollama's issue tracker skews "how do I run X model" and "why is my GPU not being used." Open WebUI's issue tracker skews "how do I connect it to Y" and "feature request: Z." Neither project is going anywhere.

What we'd do

Run the docker-compose above. Pull llama3.1:8b or qwen2.5:7b. Have a real chat with it in Open WebUI within 20 minutes. Then decide whether you actually need the whole stack, only the runtime, or only the UI. The setup is cheap enough to explore.

The wrong answer is picking one, hitting its limit, and blaming the tool. They're two halves of the same idea.