4.6 — Production Thinking
Building something that works on your machine is Phase 1. Building something that works for real users, reliably, at cost, and that you can diagnose when it breaks — that’s production thinking.
The Production Checklist
Section titled “The Production Checklist”| Concern | What It Means | Why It Matters |
|---|---|---|
| Reliability | Does it work consistently? What happens when something fails? | Users don’t forgive random crashes |
| Security | Who can access what? Are secrets protected? Are inputs validated? | One breach can destroy trust permanently |
| Cost | How much does each AI inference call cost? How does cost scale with users? | Inference isn’t free — every API call has a price |
| Observability | Can you see what’s happening inside your system? Logs, metrics, alerts. | You can’t fix what you can’t see |
| Recovery | If everything breaks, how quickly can you restore service? | Backups, rollback plans, incident response |
Vocabulary
Section titled “Vocabulary”| Term | Definition |
|---|---|
| Uptime | Percentage of time a system is operational — 99.9% = ~8.7 hours downtime per year |
| SLA (Service Level Agreement) | A commitment to a specific level of reliability |
| Incident | An unplanned event that disrupts service |
| Rollback | Reverting to a previous working version after a bad deploy |
| Monitoring | Continuous automated checking of system health |
| Alert | An automated notification when something goes wrong |
| Audit trail | A record of who did what and when — critical for security and compliance |
Production thinking is not something you bolt on at the end. Design for it from the start, especially the cost dimension. AI inference at scale is not free — a system that looks cheap at 10 users can become expensive at 10,000.
Next: 4.7 — The Full Stack | Phase overview: Phase 4