The gap between AI demos and production AI is bigger than most realize

AI demos work 90% of the time. Production AI systems need to work 99.9% of the time.

That gap is where the real engineering happens.

Things that matter in production that demos skip:

Latency budgets. A demo can take 30 seconds. Production workflows need responses in under 5 seconds. This changes your architecture significantly.
Cost management. A single LLM call in a demo costs pennies. 10,000 calls per day at $0.50/1M tokens adds up fast. You need caching, batching, and model tiering.
Failure modes. LLMs hallucinate, APIs timeout, models get deprecated. Production systems need graceful degradation for every failure mode.
Monitoring. You can't fix what you can't see. Every LLM call needs logging, latency tracking, and output quality checks.
Evolution. Models improve, APIs change, business rules evolve. Your system needs to adapt without rewrites.

The hardest lesson: building a reliable AI system is 20% AI and 80% infrastructure.

What's been your biggest lesson moving AI from prototype to production?

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The gap between AI demos and production AI is bigger than most realize