Open WebUI vs Text Generation WebUI: Self-Hosted AI Interfaces Compared

Running large language models locally is no longer a niche hobby. With consumer GPUs packing 16-24 GB of VRAM and quantized models fitting in 4-8 GB, anyone with a halfway decent machine can run AI models that rival cloud services. But the model is only half the story — you also need an interface to interact with it.

Photo by Andrew Neel on Unsplash

Two projects have emerged as the dominant self-hosted AI interfaces: Open WebUI (formerly Ollama WebUI) and Text Generation WebUI (commonly called oobabooga, after its creator's username). They both let you chat with local LLMs through a web browser, but they are designed for very different users with very different goals.

The Core Difference

Open WebUI is built for people who want a ChatGPT-like experience with local models. Clean interface, conversation management, document upload, web search integration, multi-model support. If you want to replace your ChatGPT subscription with something running on your own hardware, this is your tool.

Text Generation WebUI is built for people who want fine-grained control over model loading, inference parameters, and generation behavior. Multiple backend loaders, detailed parameter tuning, training/fine-tuning tools, and extension support. If you care about the difference between top_p=0.9 and top_p=0.95, or you need to load models in specific quantization formats, this is your tool.

Think of Open WebUI as the iPhone of local AI interfaces — polished, opinionated, just works. Text Generation WebUI is the Android — configurable, flexible, sometimes messy.

Feature Comparison

Deploying Open WebUI

Open WebUI is designed to work with Ollama as its backend, though it also supports any OpenAI-compatible API.

With Ollama Backend

# docker-compose.yml
version: "3.8"

services:
  ollama:
    image: ollama/ollama:latest
    container_name: ollama
    restart: unless-stopped
    volumes:
      - ollama_data:/root/.ollama
    # For NVIDIA GPU:
    deploy:
      resources:
        reservations:
          devices:
            - driver: nvidia
              count: all
              capabilities: [gpu]

  open-webui:
    image: ghcr.io/open-webui/open-webui:main
    container_name: open-webui
    restart: unless-stopped
    ports:
      - "3000:8080"
    environment:
      OLLAMA_BASE_URL: http://ollama:11434
      WEBUI_SECRET_KEY: your-secret-key-here
    volumes:
      - open_webui_data:/app/backend/data
    depends_on:
      - ollama

volumes:
  ollama_data:
  open_webui_data:

docker compose up -d

# Pull a model
docker exec ollama ollama pull llama3.2
docker exec ollama ollama pull mistral

Navigate to http://your-server:3000, create an admin account, and start chatting. The first user to register becomes the administrator.

Without Ollama (OpenAI API Compatible)

If you are running vLLM, LocalAI, or any other OpenAI-compatible backend:

services:
  open-webui:
    image: ghcr.io/open-webui/open-webui:main
    container_name: open-webui
    restart: unless-stopped
    ports:
      - "3000:8080"
    environment:
      OPENAI_API_BASE_URL: http://your-backend:8000/v1
      OPENAI_API_KEY: your-api-key
      WEBUI_SECRET_KEY: your-secret-key-here
    volumes:
      - open_webui_data:/app/backend/data

Deploying Text Generation WebUI

Text Generation WebUI has a more involved setup because it bundles its own model loading infrastructure:

# docker-compose.yml
version: "3.8"

services:
  text-gen-webui:
    image: atinoda/text-generation-webui:default-nightly
    container_name: text-gen-webui
    restart: unless-stopped
    ports:
      - "7860:7860"   # Web UI
      - "5000:5000"   # API
      - "5005:5005"   # Streaming API
    environment:
      - EXTRA_LAUNCH_ARGS=--listen --api
    volumes:
      - ./characters:/app/characters
      - ./loras:/app/loras
      - ./models:/app/models
      - ./presets:/app/presets
      - ./prompts:/app/prompts
      - ./training:/app/training
      - ./extensions:/app/extensions
    deploy:
      resources:
        reservations:
          devices:
            - driver: nvidia
              count: all
              capabilities: [gpu]

volumes: {}

mkdir -p characters loras models presets prompts training extensions
docker compose up -d

Downloading Models

Text Generation WebUI has a built-in model downloader in the UI, or you can download models manually:

# Using the built-in downloader (via UI)
# Go to the Model tab -> Download -> paste HuggingFace model name

# Or download manually
cd models
# Example: downloading a GGUF quantized model
wget https://huggingface.co/TheBloke/Mistral-7B-Instruct-v0.2-GGUF/resolve/main/mistral-7b-instruct-v0.2.Q4_K_M.gguf

The Chat Experience

Open WebUI

Open WebUI feels immediately familiar to anyone who has used ChatGPT. The left sidebar shows your conversation history with search. The main panel is a clean chat interface with markdown rendering, code syntax highlighting, and file attachments.

Key UX features:

• Model selector: Switch between any loaded Ollama model with a dropdown. No reloading, no waiting.
• System prompts: Create and save custom system prompts (personas) that you can apply to any conversation.
• Document RAG: Upload PDFs, text files, or web pages. Open WebUI chunks and embeds them, then uses retrieval-augmented generation to answer questions about the content.
• Web search: Toggle web search for conversations that need current information. Integrates with SearXNG, Google, Brave, and others.
• Artifacts: Code blocks get a "run" button for HTML/JS, and mathematical expressions render with LaTeX.
• Collaborative features: Share conversations with other users on the same instance.

Text Generation WebUI

The interface is more utilitarian. It is built with Gradio (a Python ML UI framework), which gives it a distinctive "research tool" look. There are multiple tabs:

• Chat: Conversation interface with character support
• Default: Raw text completion (no chat formatting)
• Notebook: Extended text editing and generation
• Parameters: The real power — dozens of generation parameters you can tune in real time

The chat mode supports character cards (like SillyTavern) with system prompts, example dialogues, and persona definitions. The Default and Notebook modes give you raw access to the model's completion capabilities without chat formatting, which is essential for creative writing, code generation, and other non-conversational tasks.

Model Loading and Backends

This is where Text Generation WebUI significantly outpaces Open WebUI.

Open WebUI (via Ollama)

Ollama handles model management transparently. You pull models with ollama pull, and they just work. Ollama uses llama.cpp under the hood, which means it supports GGUF quantized models. GPU offloading is automatic.

This simplicity is both the strength and limitation. You cannot:

• Load models in other formats (GPTQ, AWQ, EXL2)
• Control layer-by-layer GPU offloading
• Use custom quantization settings
• Mix different inference backends

Text Generation WebUI

Text Generation WebUI supports multiple loaders, each with different strengths:

The practical impact: if you have a 12 GB GPU and want to run a 13B parameter model, Text Generation WebUI lets you choose between a GGUF 4-bit quantization (runs on CPU+GPU), an EXL2 4-bit quantization (runs entirely on GPU, faster), or a GPTQ quantization (GPU, good compatibility). Each produces different quality/speed tradeoffs that you can evaluate for your specific use case.

Parameter Control

Open WebUI

Basic but sufficient for most users:

• Temperature
• Top P
• Top K
• Max tokens
• Repeat penalty

These are accessible from the chat settings and cover the parameters that actually matter for day-to-day use.

Text Generation WebUI

Extensive parameter control for those who need it:

Temperature, Top P, Top K, Typical P, Min P,
Repetition Penalty, Frequency Penalty, Presence Penalty,
Repetition Penalty Range, Encoder Repetition Penalty,
No Repeat N-gram Size, Mirostat (mode, tau, eta),
DRY (multiplier, base, allowed length, sequence breakers),
Top A, Epsilon Cutoff, Eta Cutoff, Smoothing Factor,
Temperature Last, Dynamic Temperature (low, high, exponent),
Seed, Context Length, Max New Tokens, Truncation Length,
Ban EOS Token, Add BOS Token, Skip Special Tokens,
Grammar (GBNF), Guidance Scale, Negative Prompt

If you know what these do, Text Generation WebUI is indispensable. If you do not, Open WebUI's defaults are fine and you are not missing anything for normal chat use.

Resource Requirements

Open WebUI is significantly lighter on the UI side because it delegates model management to Ollama (a Go binary) rather than bundling the entire PyTorch ecosystem.

Multi-User and Security

Open WebUI has proper multi-user support:

• User registration with admin approval
• Role-based access (admin, user)
• Per-user conversation history
• Model access controls
• SSO support (OAuth, OIDC)

Text Generation WebUI has minimal user management:

• Optional basic authentication (--gradio-auth user:password)
• No per-user conversation separation
• No role-based access
• Designed as a single-user tool

If you are deploying for a household or small team, Open WebUI is the only real option.

Who Should Use What

Choose Open WebUI if:

• You want a ChatGPT replacement for daily use
• Multiple people will use the instance
• You value a polished, intuitive interface
• Document upload and web search are important
• You want the simplest deployment and maintenance
• Ollama's model support covers your needs

Choose Text Generation WebUI if:

• You experiment with different model formats and quantizations
• You need fine-grained control over generation parameters
• You are doing model evaluation or benchmarking
• You want to fine-tune or train LoRA adapters
• You use character cards or roleplay scenarios
• You need raw text completion (not just chat)
• You are a researcher or developer working on LLM applications

Use both if:

• You run Open WebUI for daily chat and Text Generation WebUI for experimentation. They can share the same GPU (not simultaneously) and coexist on the same server.

Running Both on One Server

If you want both tools available, stagger their GPU usage:

# Open WebUI + Ollama on port 3000 (daily use)
# Text Generation WebUI on port 7860 (experimentation)
# Share the same GPU, but only run one inference at a time

In practice, Ollama is good at releasing GPU memory when idle, so you can chat with Open WebUI, then switch to Text Generation WebUI for parameter tuning, and they will coexist without conflict — as long as you are not generating with both simultaneously.

Final Thoughts

The local AI interface space is maturing rapidly. Open WebUI has become the de facto standard for anyone who wants "ChatGPT but local" — it is polished, feature-rich, and absurdly easy to deploy. Text Generation WebUI remains essential for power users who need the control and flexibility that a streamlined chat interface deliberately hides.

Both projects are actively developed with frequent releases. Both have large, helpful communities. And both are free, open-source software that keeps your AI conversations entirely on your own hardware.

For most self-hosters, start with Open WebUI and Ollama. If you find yourself wanting to experiment with different model formats, tweak generation parameters, or do any kind of model development, add Text Generation WebUI alongside it. The two tools complement each other perfectly.