The Production AI Checklist: 12 Things Most Teams Skip

The gap between a working notebook and a production-grade AI system is wider than most teams expect. Here are the twelve checkpoints we run through before any model goes live.

Observability First

Before deployment, instrument your model with input distribution monitoring, prediction confidence tracking, and latency percentile logging. A model that degrades silently is worse than one that fails loudly. Tools like Arize, WhyLabs, or even a custom Snowflake logging layer give you the visibility to catch drift before users do.

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Most model failures in production are not accuracy failures — they're data pipeline failures. Monitor your inputs as rigorously as your outputs.

Feedback Loops and Retraining Triggers

Define your retraining trigger before you go live, not after. Whether it's a drift threshold, a scheduled cadence, or a performance SLA breach, the trigger should be automated and version-controlled. Manual retraining decisions become bottlenecks as you scale.

Cost Modelling

For LLM-based features specifically, model the token cost at P95 and P99 traffic levels before launch. A feature that costs $0.002 per call sounds cheap — at 2 million calls per day it's $4,000 daily. Build cost dashboards alongside performance dashboards from day one.