Architecture guide with diagrams for inter-service networking
Version-pinned configuration files (no :latest)
Backup & restore scripts with MinIO as the backup target
Pre-configured Grafana dashboards for the entire stack
Resource limit defaults to prevent Ollama memory starvation
Zero-downtime update rollouts and version upgrade paths
Changelog with v1.0.0 → v1.2.0 upgrade history
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Disclaimer & Terms of Use
The information, code snippets, configuration files, and instructions provided in this product are shared for educational and informational purposes only. While every effort has been made to ensure accuracy, you are solely responsible for reviewing, testing, and adapting any code or configurations to your own environment before using them in production.
No liability: The author(s) shall not be held liable for any damages, data loss, system outages, security breaches, or other issues arising from the use, misuse, or inability to use the code, configurations, or instructions provided in this product. By downloading or using this product, you acknowledge that you understand and accept these terms.
Each tool has an official docker-compose. Dify has one. n8n has one. Ollama has one. Install them all separately and you get:
5 separate Docker Compose files — each with its own networking, its own database, its own port mapping
No shared authentication — Dify has its own, n8n has its own, Open WebUI has its own
No shared TLS — or 5 separate Let's Encrypt certs to manage
No shared monitoring — if n8n's workflow queue backs up, nobody knows until jobs fail
No shared backup — each database backed up independently (if backed up at all)
No resource budgets — Ollama grabs all GPU memory, Dify's Celery workers crash
This is what "free" looks like after 3 months of running: a fragile patchwork that nobody fully understands.
AI Infrastructure Mastery is the alternative: one compose file, one proxy, one monitoring dashboard, one backup target, resource budgets enforced from day one.
The Missing Manual for Your AI Homelab
Setting up Dify? You can do that in 10 minutes. Installing Ollama? curl -fsSL https://ollama.com/install.sh | sh. Spinning up n8n? A three-line Docker Compose file.
Each individual tool is well-documented. The hard part — the really hard part — is making them all talk to each other in a way that doesn't collapse the first time you look away.
A production-grade homelab isn't just a bunch of containers running on the same host. It's a system where:
Backup actually works (and you've tested the restore)
Monitoring tells you something's wrong before your users do
Secrets aren't hardcoded into Compose files
Updates don't require reading three separate changelogs and hoping
Security means TLS is automatic, not an afterthought
This guide is the missing manual — the integration layer that connects the individual stacks into a cohesive, production-ready AI infrastructure platform.
What's Inside
A complete architecture that ties together seven core components into a unified system behind a single Traefik reverse proxy with automatic SSL:
Dify — LLM application orchestration with visual workflow builder, RAG pipelines, and agent capabilities
n8n — Workflow automation connecting AI outputs to 400+ integrations (email, Slack, CRM, databases)
Ollama — Local LLM inference for privacy-sensitive workloads, with GPU acceleration
Qdrant — High-performance vector database for semantic search and RAG embeddings
Prometheus + Grafana — Real-time infrastructure monitoring with pre-built dashboards
MinIO — S3-compatible object storage for backups and artifact storage
Architecture Overview
AI Infrastructure Stack Architecture
The stack runs behind a single Traefik reverse proxy that terminates TLS and routes traffic by hostname. An internal Docker network keeps service-to-service communication isolated from the outside world. The diagram above shows how every component connects:
Traefik terminates SSL via Let's Encrypt and routes dify.yourdomain.com, n8n.yourdomain.com, etc. to the correct containers
Dify (api + worker + web) stores application data in PostgreSQL, uses Redis for the task queue, and connects to Qdrant for vector storage
n8n has dedicated PostgreSQL and Redis instances; it calls Ollama's OpenAI-compatible API for LLM-powered workflow steps
Ollama serves models on the internal network — used by both Dify and n8n
Prometheus scrapes metrics from every service; Grafana visualizes them
MinIO stores nightly backups of all databases and volumes
You'll learn:
How to wire Dify's vector store to Qdrant for production-scale RAG pipelines
How to connect n8n to Ollama's local models for workflow automation without API costs
How to configure MinIO as a backup target for every service in the stack
How Traefik auto-provisions Let's Encrypt certificates for all services with zero config
How Prometheus discovers all services and Grafana visualizes the health of the entire stack
How to restore individual services from MinIO backups without taking down the whole system
How to pin versions and apply updates with zero-downtime rollouts
How to set up resource limits so Ollama doesn't starve Dify of memory
How to configure n8n webhooks to trigger Dify workflows programmatically
How to secure inter-service communication with internal networks
Version Pin Policy
Each component in the stack is pinned to a specific version in docker-compose.yml, not :latest. This guarantees reproducibility — the stack you deploy today will work identically months from now.
Versions are reviewed and bumped quarterly. Active purchasers receive notifications when a new version of the stack is published.
Who This Is For
DevOps engineers who've already deployed individual AI stacks (Dify, n8n, Ollama) and now need to integrate them into a cohesive platform with proper monitoring, backup, and security
Homelab enthusiasts ready to move from "it works on my machine" to "it survives a power outage and an SSD failure"
Self-hosters running AI services who've learned the hard way that "I'll set up backups later" means "I've already lost data"
Technical founders building AI-powered products who want a production-grade foundation before the first customer hits the login page
If you can edit a YAML file and run docker compose up, you have everything you need to deploy this stack.
Changelog
v1.2.0 (2026-06-19)
Added Qdrant as the vector database backend (replaces Weaviate)
Updated Dify to v1.1.3 with new RAG pipeline features
Added Prometheus scrape config for Qdrant and Ollama metrics
Improved backup scripts — MinIO backup now compresses before upload
Added resource limit defaults to prevent Ollama memory starvation
v1.1.0 (2025-12-10)
Added Traefik as the reverse proxy (replaces Nginx)
Added MinIO backup target for all services
Updated n8n to v1.73.0 with sub-workflow support
Added Grafana dashboard — pre-configured for all stack services
docker-compose.yml — The complete 20+ service stack, fully commented
.env.example — Every environment variable documented with sensible defaults
README.md — Architecture overview, quick-start guide, and production runbook
All future updates to the stack are included at no additional cost. When a new version of Dify, n8n, or any component is released, the updated compose file is published as a new download.
No subscriptions. No recurring fees. One purchase, lifetime access, unlimited deployments.
The Stack That Documents Itself
You're deploying version-pinned services with changelog entries. Every service, every environment variable, every architecture decision is documented in the README. Six months from now, a new engineer joins the team and can understand the entire stack from the compose file — no tribal knowledge required.
That's the difference between "it works on my machine" and "anyone can maintain it."
