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betelgeusebytes/ARCHITECTURE.md

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BetelgeuseBytes – Architecture Overview

High-Level Architecture

This platform is a self-hosted, production-grade Kubernetes stack designed for:

  • AI / ML experimentation and serving
  • Data engineering & observability
  • Knowledge graphs & vector search
  • Automation, workflows, and research tooling

The architecture follows a hub-and-spoke model:

  • Core Infrastructure: Kubernetes + networking + storage
  • Platform Services: databases, messaging, auth, observability
  • ML / AI Services: labeling, embeddings, LLM serving, notebooks
  • Automation & Workflows: Argo Workflows, n8n
  • Access Layer: DNS, Ingress, TLS

Logical Architecture Diagram (Textual)

Internet
   │
   ▼
DNS (betelgeusebytes.io)
   │
   ▼
Ingress-NGINX (TLS via cert-manager)
   │
   ├── Platform UIs (Grafana, Kibana, Gitea, Neo4j, MinIO, etc.)
   ├── ML UIs (Jupyter, Label Studio, MLflow)
   ├── Automation (n8n, Argo)
   └── APIs (Postgres TCP, Neo4j Bolt, Kafka)

Kubernetes Cluster
   ├── Control Plane
   ├── Worker Nodes
   ├── Stateful Workloads (local SSD)
   └── Observability Stack

Key Design Principles

  • Bare‑metal friendly (Hetzner dedicated servers)
  • Local SSD storage for stateful workloads
  • Everything observable (logs, metrics, traces)
  • CPU-first ML with optional GPU expansion
  • Single-tenant but multi-project ready

Networking

  • Cilium CNI (eBPF-based networking)
  • NGINX Ingress Controller
  • TCP services exposed via Ingress patch (Postgres, Neo4j Bolt)
  • WireGuard mesh between nodes

Security Model

  • TLS everywhere (cert-manager + Let’s Encrypt)
  • Namespace isolation per domain (db, ml, graph, observability…)
  • Secrets stored in Kubernetes Secrets
  • Optional Basic Auth on sensitive UIs
  • Keycloak available for future SSO

Scalability Notes

  • Currently single control-plane + workers

  • Designed to add:

    • More workers
    • Dedicated control-plane VPS nodes
    • GPU nodes (for vLLM / training)

What This Enables

  • Research platforms
  • Knowledge graph + LLM pipelines
  • End-to-end ML lifecycle
  • Automated data pipelines
  • Production observability-first apps