Guide: Self-Hosted AI — Ollama, Open WebUI and LiteLLM

Guide: MinIO as an S3-Compatible Backup Target

Self-hosted S3-compatible object storage for backups: MinIO behind an Nginx proxy, with an rclone-based backup agent and versioned buckets. Your offsite backups without a cloud bill.

Guide: Production k3s Cluster with Rancher, Longhorn and MetalLB

A complete Kubernetes lab-to-production pattern: k3s in Docker with the embedded etcd datastore, Rancher for management, MetalLB for Layer-2 LoadBalancer IPs, Longhorn replicated storage, and Traefik

Run LLMs on your own GPU hardware: Ollama as the serving engine, Open WebUI as the ChatGPT-style frontend, and LiteLLM as an OpenAI-compatible router in front of everything — so local models and external APIs stay interchangeable.

What this is

A deployment guide with working examples — not a downloadable product. Everything below is the actual configuration I use, abridged to the parts that matter.

Deployment example

services:
  ollama:
    image: ollama/ollama:${OLLAMA_VERSION:-0.5.9}
    container_name: ollama
    restart: unless-stopped
    volumes:
      - ollama_models:/root/.ollama
      - ./config/ollama:/etc/ollama:ro
    ports:
      - ${OLLAMA_PORT:-11434}:11434
    environment:
      - OLLAMA_HOST=0.0.0.0
      - OLLAMA_KEEP_ALIVE=${OLLAMA_KEEP_ALIVE:-5m}
      - OLLAMA_NUM_PARALLEL=${OLLAMA_NUM_PARALLEL:-2}
      - OLLAMA_MAX_LOADED_MODELS=${OLLAMA_MAX_LOADED_MODELS:-3}
    deploy:
      resources:
        reservations:
          devices:
            - driver: nvidia
              count: all
              capabilities:
                - gpu
    healthcheck:
      test:
        - CMD
        - curl
        - -f
        - http://localhost:11434/api/tags
      interval: 30s
      timeout: 10s
      retries: 3
    networks:
      - ai_network
  litellm:
    image: ghcr.io/berriai/litellm:${LITELLM_VERSION:-main-v1.16.18}
    container_name: litellm
    restart: unless-stopped
    depends_on:
      ollama:
        condition: service_healthy
    volumes:
      - ./config/litellm/proxy_config.yaml:/app/proxy_config.yaml:ro
    ports:
      - ${LITELLM_PORT:-4000}:4000
    environment:
      - LITELLM_MASTER_KEY=${LITELLM_MASTER_KEY:?LITELLM_MASTER_KEY is required}
      - DATABASE_URL=${LITELLM_DB_URL:-}
      - LITELLM_MODE=${LITELLM_MODE:-proxy}
      - OPENAI_API_KEY=${OPENAI_API_KEY:-}
    command:
      - --config
      - /app/proxy_config.yaml
      - --port
      - '4000'
    healthcheck:
      test:
        - CMD
        - curl
        - -f
        - http://localhost:4000/health/liveliness
      interval: 30s
      timeout: 10s
      retries: 3
    networks:
      - ai_network

Excerpt — ollama, litellm from the compose file. The remaining services (proxies, init jobs, exporters) follow the same pattern and mount their configuration from a config/ directory.

Essential environment variables

Copy .env.example to .env and at minimum set:

OLLAMA_VERSION=latest
OLLAMA_PORT=11434
OLLAMA_HOST=0.0.0.0
OLLAMA_KEEP_ALIVE=10m
OLLAMA_NUM_PARALLEL=2
OLLAMA_MAX_LOADED_MODELS=4

Production checklist

• Set strong, unique secrets in .env before the first start — never ship the example values
• Pin image versions and test upgrades on a staging copy first
• Configure and restore-test backups before you rely on them
• Put the stack behind TLS and restrict management UIs to a VPN or allowlist
• Wire the stack into monitoring and alerting from day one

A guide, not a product. This page is deployment documentation with working examples — there is no zip, no download, no support contract. You adapt the patterns to your own environment, and you own the result.

Want this running production-grade in your infrastructure instead? This is the core of the two-day Self-Hosted AI in Production workshop — details on the consulting page: €1,000/day, remote or on-site, invoice as usual.