Table of Contents
Before committing to self-hosted AI as primary infrastructure, this checklist tests readiness across team, infrastructure, data, ops dimensions. Most items are achievable; honest self-assessment helps.
15-item checklist across team (capacity, skills), infrastructure (hardware, observability), data (quality, residency), ops (eval, deploy, runbooks). Yes to 12+: ready. Yes to 8-11: partially ready, address gaps. Yes to fewer: stay on hosted API + plan to build self-hosted readiness.
Checklist
Team
- 1-2 engineers who can manage Linux + GPU infrastructure
- 1 engineer comfortable with Python production deploy
- Defined on-call rotation
- SME / domain expert available for eval curation
Infrastructure
- GPU hosting provider chosen with appropriate SKUs
- Observability stack design (Prometheus + Grafana + structured logs)
- Backup / DR strategy for vector store + configs
- Secrets management approach
Data
- Eval harness designed (200-500 representative queries)
- Data residency requirements documented
- Source data quality acceptable for RAG (cleaning planned)
- GDPR / regulatory scope understood
Ops
- Feature flag system for rollout / rollback
- Runbook templates for common incidents
- Cost monitoring + per-feature attribution planned
If yes to most
You're ready. Proceed with self-hosted; the patterns are mature; the economics will work. Allow ~2-4 weeks for production-grade setup.
If mostly no
Stay on hosted API; build readiness in parallel. Common path: 6 months on hosted while building team capability + observability practices → transition to self-hosted with confidence.
Verdict
Self-hosted AI readiness is a function of team + infrastructure + data + ops. Honest assessment against this checklist helps avoid the failure mode of premature self-hosting. The right time is when the checklist mostly says yes; before that, hosted API is the right choice.
Bottom line
Honest self-assessment; build readiness deliberately. See deployment checklist.
