The Most Expensive Thing You Didn’t Build Into Your AI Demo

When you build an LLM-powered demo, the magic moment comes fast: a model answers questions, writes content, or reasons through a problem. Everyone’s impressed. Then you take it to production, and everything slows down.

The reason? Reliability isn’t free.

In a demo, if the model fails 1 in 10 times, you try again. In production, every failure is a support ticket, a lost customer, or a business risk. Achieving 99% reliability means:

• Adding retries, fallbacks, and guardrails.
• Handling bad inputs gracefully.
• Monitoring model performance over time.
• Designing for degraded mode if an upstream service is down.

None of that shows up in the demo, but all of it shows up in your production budget. The difference between “working once in a demo” and “working every time for every user” is not an incremental cost; it’s a step change.

If you don’t plan for reliability early, you’ll find yourself rebuilding your AI system from scratch under pressure. The smarter approach: bake it in from day one.

Demos inspire. Reliability retains… and survives production reality.