Learning brief
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TL;DR
Running AI in production is very different from getting it working in a notebook. You need to handle latency, reliability, cost management, monitoring, and graceful degradation. The model is the easy part — the infrastructure around it is where the real engineering happens.
What Happened
The gap between 'AI demo' and 'AI product' is massive. A demo can tolerate slow responses, occasional errors, and high costs. Production can't. Teams learned this the hard way as they moved from prototypes to real products.
Key production concerns include: latency (users won't wait 30 seconds for a response), reliability (APIs go down, models return garbage), cost (a popular feature can bankrupt you at $0.01/request), monitoring (you need to know when quality degrades), and safety (you can't ship harmful outputs to users).
The AI infrastructure ecosystem has matured rapidly. Observability tools (LangSmith, Helicone, Braintrust), deployment platforms (Modal, Replicate, Together AI), and evaluation frameworks (promptfoo, RAGAS) address these challenges.
So What?
If you're building an AI feature, budget 70% of your time for production hardening and 30% for the AI logic itself. Streaming responses, caching, fallbacks, error handling, and monitoring are not optional — they're the difference between a toy and a product.
Cost optimization is critical. Techniques like prompt caching, model routing (use cheap models for easy tasks), batching, and aggressive caching can reduce costs by 10x.
Now What?
Implement streaming responses from day one — perceived latency matters more than actual latency
Add a caching layer for repeated or similar queries
Set up cost alerts and per-user rate limits before launch, not after the first bill
Use prompt versioning and A/B testing to measure changes in quality