Architecture Diagrams
This page presents a collection of visual diagrams that illustrate the architecture of my homelab and Kubernetes setup. These diagrams help clarify the relationships between hardware components, network topology, and deployed workloads, making the infrastructure design and operational flow easy to understand for both technical and non-technical audiences.
Conclusion
These architecture diagrams provide a clear, visual understanding of how my homelab and Kubernetes infrastructure are structured and operate. They serve as both a reference for future upgrades and a resource for others interested in building similar systems. If you have questions or want to see more details, feel free to reach out!
More in infrastructure-engineering
Continue exploring articles in this category.
Sep 7, 2025
K3s on Raspberry Pis
Step-by-step guide to setting up a K3s Kubernetes cluster on Raspberry Pi nodes — networking, configuration, a…
Sep 13, 2025
Hardware List and Costs
Full hardware list and cost breakdown for my ARM64 homelab Kubernetes cluster — Raspberry Pis, switches, stora…
Sep 20, 2025
Flashing Raspberry Pi OS
How to flash Raspberry Pi OS Lite and configure base settings for a production-ready Kubernetes homelab node f…
Case Study
Bible Verse — Case Study
Production SaaS Platform · Full-Stack · Founder & Sole Engineer
A domain-driven SaaS platform with five independently scalable system boundaries: scripture content delivery, RAG-backed AI study, real-time community interaction, async media processing, and infrastructure services — built and operated end-to-end.
Our Results
How We Built It
- RAG pipeline grounding AI responses in actual scripture rather than model memory
- Hybrid Llama / OpenAI routing — local inference for cost, API fallback for quality at the edge
- Non-blocking media processing — FFmpeg jobs enqueued via BullMQ, API never waits on transcoding
- Cross-instance real-time consistency via Redis pub/sub behind WebSocket and WebRTC layers
Lessons Learned
- Domain boundaries enforced at the service layer prevent coupling long before scale demands microservices.
- RAG retrieval quality matters more than model size — better embeddings outperform a larger model on poor context.
- Async queue design should be first-class, not bolted on; BullMQ worker isolation saved the request path repeatedly.
Stack
Written by
5+ years building production systems · AI, Backend & Infrastructure · Founder of Bible Logic
Full-stack engineer with 5+ years of hands-on experience designing and shipping production systems — from Nuxt 3 frontends and Nitro APIs to self-hosted Kubernetes clusters, RAG pipelines, and real-time AI applications. Everything I write comes from systems I've designed, deployed, and operated in production.