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Self-Hosting the RMM Stack

Every product runs on your own hardware. No vendor lock-in, no telemetry home calls, no license server. The standard deployment target is a single VPS (4 vCPU / 8 GB RAM) running Docker + nginx.

Reference topology​

β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”
HTTPS 443 ──▢ β”‚ nginx (TLS termination, rate limiting) β”‚
β””β”€β”€β”€β”€β”€β”€β”¬β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”¬β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”¬β”€β”€β”€β”€β”˜
β”‚ β”‚ β”‚
β”Œβ”€β”€β”€β”€β”€β”€β–Όβ”€β”€β”€β”€β”€β” β”Œβ”€β”€β”€β”€β”€β”€β–Όβ”€β”€β”€β”€β”€β”€β” β”Œβ”€β”€β”€β”€β”€β–Όβ”€β”€β”€β”€β”€β”€β”
β”‚ rmi-backendβ”‚ β”‚ degenfeed β”‚ β”‚ pryscraper β”‚ (Docker)
β”‚ :8000 β”‚ β”‚ :3000 β”‚ β”‚ :8080 β”‚
β””β”€β”€β”€β”€β”€β”€β”¬β”€β”€β”€β”€β”€β”˜ β””β”€β”€β”€β”€β”€β”€β”¬β”€β”€β”€β”€β”€β”€β”˜ β””β”€β”€β”€β”€β”€β”¬β”€β”€β”€β”€β”€β”€β”˜
β”‚ β”‚ β”‚
β”Œβ”€β”€β”€β”€β”€β”€β–Όβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β–Όβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β–Όβ”€β”€β”€β”€β”€β”€β”
β”‚ Postgres Β· Redis Β· Qdrant (vectors) β”‚
β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜

Per-product guides​

ProductGuide
RMI backendRMI deployment
DegenFeedDegenFeed deployment
PryPry deployment
WalletPressWalletPress deployment
rugmunchbotrugmunchbot deployment
RugBrainRugBrain installation
rmi-mcp-x402rmi-mcp-x402 installation
x402-cofferx402-coffer installation

Baseline requirements​

  • Docker Engine 24+ and Docker Compose v2
  • Postgres 15+, Redis 7+, Qdrant 1.9+ (only where a product needs them)
  • TLS via Let's Encrypt (acme.sh or certbot) β€” never self-signed in production
  • Secrets in a secret manager or environment injection β€” never in .env files committed to git
warning

Public-facing deployments: enable rate limiting, CORS allow-lists, and follow the security operations guide. Sample configs in each repo's docker-compose.yml are development defaults, not production-ready.