How to structure an AI deployment that won’t collapse under its own weight.
For Solo Implementers
One person, no budget, no dedicated IT team. The most common scenario and the most fragile.
Key concepts:
- Self-Hosted AI — Reduce dependency on external APIs
- Human-in-the-Loop — Keep a safety net until you’re confident
- Scope Creep — Start narrow, expand slowly
For Small Teams (2-10 people)
Shared workflows, overlapping responsibilities, and the first governance challenges.
Key concepts:
- Shadow AI — Govern what you can’t see
- Adoption Stall — Drive usage or watch it die
- Silent Failure — Catch problems before they compound
Layer Hubs
- Infrastructure Layer — Self-hosting vs cloud, GPU sizing
- Automation Layer — Agents, workflows, HITL
- Strategy & Planning — Budgets, scope, TCO
Medium Team Patterns (10-50 people)
Multiple users hitting shared AI infrastructure, first real governance requirements, cost visibility needed per team.
- LiteLLM — Route traffic across models; enforce per-team spend limits with virtual keys
- Langfuse — Trace agent decisions across multiple users and sessions; catch drift before it compounds
- Open WebUI — Give teams a ChatGPT-like interface over self-hosted models without individual API access
- Vendor Lock-In — Medium teams are when lock-in costs become real; evaluate exit paths before committing
Scaling Considerations
- Model Quantization — Squeeze more users from the same GPU by choosing the right quantization level
- Infrastructure Layer — Self-hosting vs cloud, GPU sizing, and the breakeven math
Each pattern involves real tradeoffs between cost, control, and complexity. The right pattern for a 15-person team looks nothing like the right pattern for a solo implementer.