Open WebUINew

A self-hosted web interface for local models with multi-user support and permissions — commonly paired with Ollama as an internal ChatGPT.

  • Popularity
  • Local Models
  • Self-hosted
  • Open Source
  • Teams
  • Coding
  • Free
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At a glance

A proper chat interface in front of local models, shareable across a team — the layer most internal AI assistants are built on.

  • Free tierYesOpen source, self-hosted
  • Open sourceYes
  • Works in ChinaYesSelf-hostable inside China
  • APIPartialConnects to backend inference services
Best for
  • Teams offering a shared AI interface on an internal network
  • Ollama users who want a better frontend
  • Cases where data must stay on your own servers
Pros
  • Open source and self-hosted, with an interface close to mainstream chat products
  • Multi-user support and permissions for a shared deployment
  • Connects to Ollama and OpenAI-compatible backends, so models are swappable
Cons
  • Deployment needs Docker and basic operations skills
  • Ships no inference engine — performance depends on your backend and hardware
Pricing

Free and open source to self-host; the cost is servers and operations.

Pricing changes over time; check the official site

Access from mainland China

Can be deployed on servers inside China against local models or domestic provider APIs.

Open WebUI is a self-hosted web chat interface that puts a usable front end on local or self-run model services. The common pairing is Ollama serving the model and Open WebUI serving the interface, which yields an internal assistant that feels close to a mainstream chat product.

Why the Interface Layer Matters

A command line is fine for the engineer who set it up. The moment colleagues are involved, you need saved conversations, model switching, permissions and shared prompts. That is exactly the layer Open WebUI fills: multiple users, access control, and an interface nobody needs training for.

It ships no inference engine of its own — speed depends on the backend and the hardware. It solves usability, not performance.

A Typical Deployment

Run it in Docker with the backend pointed at Ollama or any OpenAI-compatible endpoint. For teams whose data can't leave the network the value is direct: the model runs on your machines, the interface runs on your server, and everything happens in a browser without leaving the building.

The cost is operations — containers, upgrades, backups, accounts. Someone has to own it, or in six months it becomes the box nobody dares touch.