Open-WebUI
Self-HostedOpen-source, self-hosted frontend for LLMs (local & remote)
Overview
Open-WebUI is an open-source, self-hosted frontend for interacting with large language models (LLMs). It offers an intuitive chat interface with markdown support, model switching, and customizable prompt templates. Deployable via Docker (one-line command), Kubernetes, or direct installation, it integrates with local LLMs (Ollama, LlamaCpp) and remote APIs (OpenAI, Anthropic). Ideal for privacy-conscious users, it lets you control your data while accessing both local and cloud-based models, supporting multiple users and extensions.
Self-Hosting Resources
Below is a reference structure for docker-compose.yml.
⚠️ Do NOT run blindly. Replace placeholders with official values.
version: '3'
services:
open_webui:
image: <OFFICIAL_IMAGE_NAME>:latest
container_name: open-webui
ports:
- "8080:<APP_INTERNAL_PORT>"
volumes:
- ./data:/app/data
restart: unless-stopped Key Features
- Intuitive chat interface with markdown & media support
- Supports local (Ollama) and remote LLMs (OpenAI, Anthropic)
- Customizable prompt templates & model switching
Frequently Asked Questions
? Is Open-WebUI hard to install?
No—Open-WebUI offers simple Docker deployment (one-line command) and integrates seamlessly with Ollama for local LLMs. Docker is recommended for non-technical users for quick setup.
? Is it a good alternative to ChatGPT?
Yes—Open-WebUI provides a similar chat experience but with self-hosted privacy and support for multiple LLMs (local or remote). It lacks some enterprise features but is ideal for personal/team use.
? Is Open-WebUI completely free?
Yes—Open-WebUI is open-source under the MIT License, so it’s free to use, modify, and self-host with no subscription fees or hidden costs.
Top Alternatives
People Also Ask about Open-WebUI
Tool Info
Pros
- ⊕ Privacy-focused (full control over user data)
- ⊕ Easy deployment via Docker for quick setup
Cons
- ⊖ Requires server/hardware resources for self-hosting
- ⊖ Needs technical setup for local LLM integration