Open WebUI vs Text Generation WebUI: Compared

Quick Verdict

Open WebUI is the better choice for most people who want a ChatGPT-like interface for self-hosted LLMs. It has multi-user support, RAG, web search, and a polished UI out of the box. Text Generation WebUI (Oobabooga) is the better choice if you need deep model control — quantization options, training, and fine-tuning. Think of Open WebUI as the “use LLMs” tool and Text Generation WebUI as the “tinker with LLMs” tool.

Overview

Both are web-based frontends for running large language models locally. Neither is a model — they’re interfaces that connect to backends like Ollama, llama.cpp, or run models directly.

Open WebUI — MIT license. 70k+ GitHub stars. Written in Python (SvelteKit frontend). Created as “Ollama WebUI,” now a standalone project supporting multiple backends. Extremely active development with weekly releases (February 2026).

Text Generation WebUI (Oobabooga) — AGPL-3.0 license. 41k+ GitHub stars. Written in Python (Gradio frontend). Created by oobabooga. Supports the widest range of model formats and loading backends. Active development (February 2026).

Feature Comparison

Feature	Open WebUI	Text Generation WebUI
Interface style	ChatGPT-like (SvelteKit)	Tabbed Gradio UI
Learning curve	Low	Medium-high
Multi-user support	Yes (RBAC, admin panel)	No (single user)
RAG (document chat)	Yes (built-in)	Via extensions
Web search integration	Yes (built-in)	Via extensions
Image generation	Yes (DALL-E/SD integration)	Via extensions
Voice input/output	Yes (STT/TTS built-in)	Via extensions
Plugin/tool system	Yes (Functions, Tools, Pipelines)	Yes (Extensions)
Model format: GGUF	Yes (via Ollama)	Yes (via llama.cpp loader)
Model format: GPTQ	No (via backend)	Yes (native)
Model format: AWQ	No (via backend)	Yes (native)
Model format: EXL2	No (via backend)	Yes (via ExLlamaV2)
Model training/fine-tuning	No	Yes (LoRA training built-in)
OpenAI-compatible API	Yes	Yes
Ollama backend	Yes (primary)	Yes (supported)
llama.cpp backend	Via Ollama	Yes (native)
Transformers backend	Via pipeline	Yes (native)
Chat templates	Yes	Yes (Jinja2)
Conversation branching	Yes	No
Markdown rendering	Yes	Yes
Docker support	Yes (official image)	Community images
Default port	3000 (was 8080)	7860
GPU required	No (connects to backend)	Recommended
License	MIT	AGPL-3.0

Installation Complexity

Open WebUI has an official Docker image and is straightforward to deploy:

services:
  open-webui:
    image: ghcr.io/open-webui/open-webui:main
    container_name: open-webui
    ports:
      - "3000:8080"
    volumes:
      - open-webui:/app/backend/data
    environment:
      - OLLAMA_BASE_URL=http://ollama:11434
    depends_on:
      - ollama
    restart: unless-stopped

  ollama:
    image: ollama/ollama:0.6.2
    container_name: ollama
    ports:
      - "11434:11434"
    volumes:
      - ollama_data:/root/.ollama
    restart: unless-stopped

volumes:
  open-webui:
  ollama_data:

Open WebUI connects to Ollama (or any OpenAI-compatible API) as its backend. It doesn’t load models itself — Ollama handles that.

Text Generation WebUI is typically installed from source:

git clone https://github.com/oobabooga/text-generation-webui
cd text-generation-webui
# Linux:
./start_linux.sh
# Windows:
start_windows.bat

The startup script creates a conda environment, installs PyTorch with CUDA support, and downloads dependencies. Models go in the models/ directory. Community Docker images exist but aren’t officially maintained.

Open WebUI is significantly easier to deploy. Text Generation WebUI’s source-based installation gives more control but requires more setup.

Performance and Resource Usage

Open WebUI is lightweight because it’s just a frontend. The UI itself uses ~200-500 MB RAM. All the heavy lifting (model inference) happens in the backend (Ollama, llama.cpp, etc.). This separation means you can run Open WebUI on a weak machine and point it at a powerful GPU server.

Text Generation WebUI loads models directly into its own process. Resource usage depends entirely on the model and loader:

• GGUF via llama.cpp: Uses system RAM + partial GPU offloading. A 7B Q4 model needs ~4-6 GB.
• GPTQ/AWQ/EXL2: Fully GPU-loaded. A 7B model needs ~4-6 GB VRAM.
• Transformers: Full precision needs ~14 GB VRAM for 7B, ~28 GB for 13B.

The architectural difference matters: Open WebUI separates the UI from inference, letting you scale independently. Text Generation WebUI bundles everything together.

Community and Support

Open WebUI: 70k+ stars with explosive growth. Very active Discord. Extensive documentation. Plugin ecosystem growing rapidly (Pipelines, Functions). One of the fastest-growing open-source AI projects.

Text Generation WebUI: 41k stars, mature community. Good wiki documentation. Large extension ecosystem. Has been the standard local LLM interface since 2023. Reddit communities (r/LocalLLaMA) frequently reference it.

Open WebUI has the momentum. Text Generation WebUI has the legacy and depth.

Use Cases

Choose Open WebUI If…

• You want a ChatGPT-like experience with local models
• You need multi-user support (family, team, organization)
• You want built-in RAG, web search, and image generation
• You’re using Ollama as your backend
• You want the easiest setup experience
• You need conversation branching and management
• You want a polished, modern UI

Choose Text Generation WebUI If…

• You want to train or fine-tune LoRA adapters
• You need to load GPTQ, AWQ, or EXL2 quantized models directly
• You want to experiment with different loading backends (ExLlamaV2, AutoGPTQ, etc.)
• You need detailed control over generation parameters (samplers, token bans, etc.)
• You want the widest model format compatibility
• You’re doing AI research or model development

Final Verdict

Open WebUI is the better choice for using LLMs. If your goal is to chat with AI models, search documents, generate images, and share access with others — Open WebUI does all of this with a clean interface and minimal setup. The Ollama integration works well, and the plugin system extends it further.

Text Generation WebUI is the better choice for working with LLMs. If you need LoRA training, want to compare quantization methods, or need to load models in formats Ollama doesn’t support — Text Generation WebUI gives you that control. It’s the Swiss Army knife of local LLM interfaces.

For most self-hosters: start with Open WebUI + Ollama. Move to Text Generation WebUI when you need model-level control that Open WebUI can’t provide.

Related

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• Best Self-Hosted AI Tools
• Docker Compose Basics
• Getting Started with Self-Hosting