Chapter 0.4 — Demos vs production¶
🎯 Chapter objective¶
Show the real distance between an impressive demo and a system in production.
🧠 Core concept¶
Demos optimize for narrative impact. Production optimizes for stability under real constraints. The two requirements rarely converge.
| Dimension | Demo | Production |
|---|---|---|
| Data | Carefully chosen | Real, dirty, with PII |
| Latency | Tolerant | Strict SLA |
| Cost | Marginal | Visible and monitored |
| Errors | Anecdotal | Formal metric |
| Operation | Manual | 24/7 with SLO |
| Versioning | Nonexistent | Mandatory |
| Security | Implicit | Auditable |
🚨 Failure modes¶
- Approving an architecture based on a demo.
- Estimating the production timeline from the demo timeline.
- Underestimating the cost of continuous operation.
🛡️ Controls and mitigations¶
- Define production criteria before starting the project.
- Treat the demo as a "spike", not an evolvable prototype.
- Reserve engineering time for the path between demo and production (which is where the real work is).
🧰 Related practical example (planned)¶
EX-FUND-04— compare the time, cost and infrastructure of a notebook classifier vs the same classifier exposed via FastAPI with basic observability.
📌 Checklist¶
- [ ] Was the demo built with data representative of production?
- [ ] Are the acceptance criteria for production written down?
- [ ] Has the operational cost been estimated and approved?