Table of Contents
Self-hosted AI deployments evolve through predictable stages. This is the roadmap.
TL;DR
MVP: RTX 5060 Ti or 3090 + Ollama / single vLLM. Production: RTX 5090 + LiteLLM + monitoring. Enterprise: multi-server + load balancer + multi-region. Most teams stall at production stage; the leap to enterprise is real ops investment.
Three stages
- Stage 1 (MVP): 1 GPU, Ollama or simple vLLM, no auth, no metrics
- Stage 2 (Production): 1 GPU, vLLM + LiteLLM + Prometheus + systemd, eval harness
- Stage 3 (Enterprise): Multi-server, load balancer, monitoring, runbook, DR plan
Milestones
- ~10 users → upgrade from MVP to production
- ~50 users → tune vLLM config, add observability
- ~500 users → multi-server
- ~5,000 users → multi-region
Verdict
Don't skip stages. Don't over-engineer for stage 3 when you're at stage 1.
Bottom line
Build the right architecture for your scale. See production AI inference server.
