# How to Run Local LLMs with Open WebUI on Docker (Ditch the 0/mo AI Subscriptions)

> Source: <https://dev.to/enfernandes/how-to-run-local-llms-with-open-webui-on-docker-ditch-the-0mo-ai-subscriptions-1d4h>
> Published: 2026-08-15 08:45:40+00:00

*Originally published on SelfHostStack*

If you pay $20/month for ChatGPT Plus or Claude Pro, you are paying $240 every single year for:

With modern quantized open-weight models (Llama 3.1, Mistral NeMo, DeepSeek-Coder, and Qwen 2.5), you can host your own private, unrestricted AI workspace on a low-cost VPS with **Open WebUI** and **Ollama**.

In this guide, we'll walk through the complete deployment using Docker Compose, setup reverse proxy authentication, and benchmark token generation speeds.

| Model / Setup | Cost | Privacy / Retention | Context & Feature Limits |
|---|---|---|---|
ChatGPT Plus / Claude Pro |
$20/month ($240/yr) | Cloud training, data retained | Strict 3-hour message caps, hard rate limits |
Open WebUI + Ollama (VPS) |
~$6 - $12/month VPS | 100% Private (Zero telemetry) | Unlimited messages, custom system prompts, RAG document chat |
Hybrid (Open WebUI + OpenRouter/DeepSeek API) |
~$1 - $3/month (Pay per token) | Zero retention API options | Access to 100+ models in a unified ChatGPT-like UI |

**Open WebUI** (70k+ GitHub stars) is arguably the most polished open-source AI frontend available:

Here is the exact production-ready `docker-compose.yml`

to run Open WebUI paired with Ollama:

```
version: '3.8'
services:
  open-webui:
    image: ghcr.io/open-webui/open-webui:main
    container_name: open-webui
    restart: always
    ports:
      - "3000:8080"
    environment:
      - OLLAMA_BASE_URL=http://ollama:11434
      - WEBUI_SECRET_KEY=generate_random_secret_string_here
      - ENABLE_SIGNUP=false # Set to false after creating your admin account
    volumes:
      - webui-data:/app/backend/data
    depends_on:
      - ollama

  ollama:
    image: ollama/ollama:latest
    container_name: ollama
    restart: always
    ports:
      - "11434:11434"
    volumes:
      - ollama-models:/root/.ollama

volumes:
  webui-data:
  ollama-models:
```

For lightweight models (like `llama3.2:3b`

or `qwen2.5-coder:1.5b`

), a standard 4GB RAM cloud instance works great. For larger 8B parameter models, choose a VPS with 8GB RAM and fast NVMe storage:

SSH into your server and run:

```
# Install Docker Engine
curl -fsSL https://get.docker.com | sh

# Create directory and start stack
mkdir -p ~/ai-stack && cd ~/ai-stack
nano docker-compose.yml # (paste the compose YAML above)
docker compose up -d
```

Pull lightweight, high-performance models directly inside the Ollama container:

```
# Ultra-fast coding model (Qwen 2.5 Coder 7B)
docker exec -it ollama ollama pull qwen2.5-coder:7b

# General reasoning model (Llama 3.1 8B)
docker exec -it ollama ollama pull llama3.1:8b

# Extremely fast 3B model for low-resource VPS
docker exec -it ollama ollama pull llama3.2:3b
```

Use Caddy for automatic HTTPS:

```
ai.yourdomain.com {
    reverse_proxy localhost:3000
}
```

If you need occasional access to frontier models (Claude 3.5 Sonnet, GPT-4o, or DeepSeek-V3) for massive refactoring tasks, you don't need a $20/month subscription:

Looking to replace other expensive developer and productivity tools? Explore our curated guides with verified Docker templates: