Model Evaluation and Monitoring in Production: What Most Teams Get Wrong

Most AI teams are good at building models and terrible at monitoring them. The gap between "works in a notebook" and "reliable in production" is where real engineering discipline lives.

Evaluation Is Not Just Accuracy

Production evaluation means tracking latency, cost, consistency, safety, and user satisfaction — not just accuracy on a test set. A model that's 95% accurate but costs 10x more per query than needed, or takes 8 seconds to respond, is not production-ready.

Drift Is Inevitable

Models degrade over time. User behavior changes, data distributions shift, and the world evolves. Without automated monitoring that detects quality degradation, teams often don't realize a model is underperforming until users complain — or worse, leave.

Cost Controls Are Engineering, Not Finance

AI infrastructure costs can scale unexpectedly. Caching strategies, model routing (using smaller models for simpler queries), and quota management are engineering problems that need engineering solutions. If your AI cost optimization lives in a spreadsheet, you're already behind.