Open WebUI vs Ollama Web UI: Which Interface is Right for You

TL;DR

Open WebUI (formerly Ollama WebUI) is the actively maintained, feature-rich choice for most users, while Ollama Web UI refers to the deprecated original project that’s no longer developed.

Open WebUI offers a ChatGPT-like interface with multi-user support, RAG (Retrieval-Augmented Generation) for document chat, model management, conversation history, and plugin architecture. It runs as a Docker container or Python application, connecting to your local Ollama instance on port 11434. Perfect for teams, homelab setups, or anyone wanting a polished UI with authentication and persistent storage.

Ollama Web UI was the original proof-of-concept that evolved into Open WebUI. If you encounter references to it, they’re outdated—the project was renamed and completely rebuilt in 2024.

Choose Open WebUI if you need:

Multi-user authentication and role-based access
Document upload and RAG capabilities
Conversation history and search
Model switching without CLI commands
API access for automation with tools like Ansible or Python scripts

Stick with Ollama CLI if you prefer:

Minimal overhead (no web server)
Scripting with curl or direct API calls
Integration into existing automation (Terraform, systemd services)

Installation Example

# Open WebUI with Docker (recommended)
docker run -d -p 3000:8080 \
  -v open-webui:/app/backend/data \
  --add-host=host.docker.internal:host-gateway \
  --name open-webui \
  ghcr.io/open-webui/open-webui:main

⚠️ Caution: When using AI-generated system commands through any interface, always review outputs before executing with sudo or on production systems. LLMs can hallucinate package names, file paths, or dangerous command combinations. Test in isolated environments first, especially when integrating with infrastructure-as-code tools like Ansible playbooks or Kubernetes manifests.

Understanding the Two Interfaces

Before diving into comparisons, let’s clarify what these interfaces actually are and how they’ve evolved in the local AI ecosystem.

Open WebUI (formerly Ollama WebUI before its 2024 rebrand) has become the de facto web interface for local LLM deployments. It’s a standalone application that connects to Ollama’s API and provides a ChatGPT-like experience with features including:

Multi-model conversations with model switching mid-chat
RAG (Retrieval Augmented Generation) with document uploads
User authentication and conversation history
Custom prompt templates and model parameters
Integration with external APIs (OpenAI, Anthropic, Groq)

docker run -d -p 3000:8080 \
  -v open-webui:/app/backend/data \
  --name open-webui \
  ghcr.io/open-webui/open-webui:main

Ollama’s Native Web Interface

Ollama itself provides a minimal REST API without a built-in web UI. When people refer to “Ollama Web UI,” they’re typically talking about either:

Third-party web clients that directly consume Ollama’s API
The original ollama-webui project (now archived, superseded by Open WebUI)
Simple HTML/JavaScript frontends developers build for specific use cases

Ollama’s API is intentionally minimal:

curl http://localhost:11434/api/generate -d '{
  "model": "llama3.2",
  "prompt": "Why is the sky blue?",
  "stream": false
}'

⚠️ Caution: When using AI models to generate system commands through any interface, always review the output before execution. LLMs can hallucinate package names, file paths, or dangerous command combinations. Test AI-generated Ansible playbooks, Docker Compose files, or shell scripts in isolated environments first—never run them directly on production infrastructure without validation.

Feature Comparison: What Sets Them Apart

While both interfaces connect to Ollama’s backend, their feature sets diverge significantly for different use cases.

Open WebUI operates as a comprehensive AI platform with user authentication, conversation persistence, and RAG (Retrieval-Augmented Generation) capabilities. You can upload PDFs, connect external knowledge bases, and create custom model pipelines. It supports multiple users with role-based access control—essential for team deployments.

# Open WebUI with persistent storage and auth
docker run -d -p 3000:8080 \
  -v open-webui:/app/backend/data \
  -e WEBUI_AUTH=true \
  ghcr.io/open-webui/open-webui:main

Ollama Web UI (now deprecated in favor of Open WebUI) provided a minimal chat interface without authentication or document processing. It was essentially a single-user frontend for quick model testing.

Advanced Integration Capabilities

Open WebUI excels at API integrations. You can configure OpenAI-compatible endpoints, allowing tools like LangChain or AutoGen to interact with your local models:

from openai import OpenAI

client = OpenAI(
    base_url="http://localhost:3000/api/v1",
    api_key="your-open-webui-api-key"
)

response = client.chat.completions.create(
    model="llama3.2:latest",
    messages=[{"role": "user", "content": "Analyze this Terraform plan"}]
)

⚠️ Caution: When using AI to generate infrastructure commands, always review output before execution. LLMs can hallucinate dangerous commands like rm -rf / or incorrect Ansible playbook syntax that could disrupt production systems.

Open WebUI also supports custom prompt templates, model parameter presets, and webhook integrations with monitoring tools like Prometheus for tracking inference metrics. The original Ollama Web UI lacked these enterprise features entirely, making Open WebUI the clear choice for serious self-hosted deployments.

Architecture and System Requirements

Both interfaces share a common foundation but differ significantly in their architectural approach and resource demands.

Open WebUI operates as a standalone Python application with its own backend, requiring Docker or a Python 3.11+ environment. It bundles SvelteKit for the frontend and maintains its own database (SQLite by default, PostgreSQL optional) for user management, conversation history, and model configurations.

Ollama Web UI (the original project) runs as a lightweight Node.js application that acts purely as a frontend proxy to Ollama’s API. It has minimal dependencies and no database requirements.

Minimum System Requirements

For Open WebUI:

4GB RAM (8GB recommended for multi-user scenarios)
2 CPU cores
10GB disk space for the application and database
Docker 24.0+ or Python 3.11+

For Ollama Web UI:

2GB RAM
1 CPU core
500MB disk space
Node.js 18+ or Docker

Both require Ollama running separately, which needs additional resources based on your chosen models (typically 8GB+ RAM for 7B parameter models).

Deployment Patterns

Open WebUI integrates well with infrastructure-as-code tools. Here’s a basic Terraform example for cloud deployment:

resource "docker_container" "open_webui" {
  name  = "open-webui"
  image = "ghcr.io/open-webui/open-webui:main"
  
  ports {
    internal = 8080
    external = 3000
  }
  
  env = [
    "OLLAMA_BASE_URL=http://ollama:11434"
  ]
}

For monitoring both interfaces, Prometheus can scrape metrics from Ollama’s API endpoint at http://localhost:11434/api/tags. Open WebUI additionally exposes application-level metrics when configured with ENABLE_METRICS=true.

Caution: When using AI assistants to generate deployment configurations, always validate resource limits and security settings before applying to production systems. AI-generated Docker Compose files may omit critical security contexts or resource constraints.

Use Case Scenarios: Which One Fits Your Needs

Choosing between these interfaces depends on your deployment context and workflow requirements.

Ollama Web UI excels for solo developers running quick experiments. If you’re testing prompt variations with llama3.1:8b or comparing model outputs, its lightweight footprint (under 50MB) makes it ideal for laptop deployments. Launch it alongside your IDE when you need fast model access without resource overhead.

Team Collaboration & Knowledge Management

Open WebUI becomes essential when multiple users need shared access. Deploy it with Docker Compose alongside Prometheus for monitoring concurrent sessions. Teams building internal documentation systems benefit from its RAG capabilities—upload your Terraform modules or Ansible playbooks, then query them conversationally. The workspace feature lets different departments maintain separate model configurations and chat histories.

# docker-compose.yml snippet for team deployment
services:
  open-webui:
    image: ghcr.io/open-webui/open-webui:main
    environment:
      - ENABLE_RAG=true
      - ENABLE_ADMIN_PANEL=true
    volumes:
      - ./data:/app/backend/data

Production AI Assistants

For customer-facing chatbots or internal support systems, Open WebUI’s API endpoints enable integration with existing applications. Connect it to your monitoring stack (Grafana, Loki) to track model performance and user interactions.

Caution: When using AI-generated system commands, always validate output before execution. A model might suggest rm -rf /var/log/* when you meant to rotate specific logs. Implement approval workflows for any AI-suggested infrastructure changes.

Resource-Constrained Environments

Running on a Raspberry Pi 5 or older hardware? Ollama Web UI’s minimal JavaScript bundle loads faster on limited bandwidth. It pairs well with quantized models like mistral:7b-instruct-q4_0 for acceptable performance on 8GB systems.

Docker vs Native Installation Trade-offs

Both Open WebUI and Ollama Web UI support Docker and native installations, each with distinct operational implications for your self-hosted AI stack.

Docker containers add 100-300MB memory overhead per service. For resource-constrained homelab setups running multiple LLMs, this matters. Native installations eliminate this layer, giving your models direct hardware access. However, Docker’s isolation prevents dependency conflicts when running multiple AI tools like Ollama, LocalAI, and text-generation-webui simultaneously.

Maintenance and Updates

Docker simplifies updates to single commands:

docker pull ghcr.io/open-webui/open-webui:main
docker compose up -d

Native installations require manual dependency management. When Open WebUI updates its Python requirements, you’ll need to handle conflicts with system packages. Tools like Ansible can automate this:

- name: Update Open WebUI native installation
  pip:
    name: open-webui
    state: latest
    virtualenv: /opt/open-webui/venv

Backup and Migration

Docker volumes make backups straightforward. Your entire Open WebUI configuration, chat history, and model settings live in /var/lib/docker/volumes/open-webui. Native installations scatter data across /home/user/.config, /opt, and system directories.

For monitoring with Prometheus, Docker exports metrics through standardized endpoints. Native installations require custom exporters.

Security Considerations

Docker provides process isolation—if Open WebUI gets compromised, attackers face container escape challenges. Native installations run with your user privileges, offering direct filesystem access.

Caution: When using AI assistants like Claude or ChatGPT to generate Docker commands or systemd service files, always validate the output. AI models occasionally hallucinate incorrect volume mounts or environment variables that could expose your model files or API keys. Test generated configurations in isolated environments before deploying to your production homelab.

Installation and Configuration Steps

Open WebUI offers multiple deployment methods. The Docker approach provides the fastest setup:

docker run -d -p 3000:8080 \
  --add-host=host.docker.internal:host-gateway \
  -v open-webui:/app/backend/data \
  --name open-webui \
  --restart always \
  ghcr.io/open-webui/open-webui:main

For Kubernetes deployments, use Helm charts with persistent volume claims for model storage. Connect to your local Ollama instance by setting OLLAMA_BASE_URL=http://host.docker.internal:11434 in the environment variables.

Installing Ollama Web UI (Legacy)

The original Ollama Web UI follows a similar pattern but requires manual configuration:

docker run -d -p 3000:8080 \
  -e OLLAMA_API_BASE_URL=http://host.docker.internal:11434/api \
  -v ollama-webui:/app/backend/data \
  --name ollama-webui \
  ghcr.io/ollama-webui/ollama-webui:main

Configuration Essentials

Both interfaces require Ollama running locally. Verify connectivity:

curl http://localhost:11434/api/tags

Configure authentication in Open WebUI through the admin panel. For production deployments, integrate with Traefik or Nginx for SSL termination:

# docker-compose.yml snippet
services:
  open-webui:
    environment:
      - WEBUI_AUTH=true
      - WEBUI_SECRET_KEY=your-secret-key-here

⚠️ Security Warning: Never expose these interfaces directly to the internet without authentication. Both tools can execute model inference requests that consume significant resources.

Connecting Multiple Ollama Instances

Open WebUI supports connecting to remote Ollama servers for distributed inference. Add endpoints through Settings → Connections, useful for load balancing across multiple homelab nodes running different model sizes.

Monitor resource usage with Prometheus exporters to prevent OOM conditions when running large models like Llama 3.1 70B on consumer hardware.

TL;DR#

Installation Example#

Understanding the Two Interfaces#

Ollama’s Native Web Interface#

Feature Comparison: What Sets Them Apart#

Advanced Integration Capabilities#

Architecture and System Requirements#

Minimum System Requirements#

Deployment Patterns#

Use Case Scenarios: Which One Fits Your Needs#

Team Collaboration & Knowledge Management#

Production AI Assistants#

Resource-Constrained Environments#

Docker vs Native Installation Trade-offs#

Maintenance and Updates#

Backup and Migration#

Security Considerations#

Installation and Configuration Steps#

Installing Ollama Web UI (Legacy)#

Configuration Essentials#

Connecting Multiple Ollama Instances#