AI Monitoring in Production
How to monitor AI systems in production. Quality, performance, drift.
What to monitor
Quality (output correctness), performance (latency, throughput), cost (per query, per user), errors, user satisfaction.
Quality drift
Models perform differently as data and context change. Without monitoring, quality degrades unnoticed.
Tools
Datadog, Splunk with AI features, specialized AI monitoring (Arize, Fiddler, WhyLabs).
Bottom line
AI monitoring is operational discipline. Skip at cost.
Frequently asked questions
Why monitor production AI?
Quality drift, performance issues, cost surprises, errors all happen. Without monitoring, problems undiscovered until customer or business impact.
Best AI monitoring tools?
Arize, Fiddler, WhyLabs specialized for AI. Datadog and Splunk general with AI features. Most enterprises combine.
What's quality drift?
AI performance degrading over time as data and context change. Common in production. Requires retraining or refinement.
Cost monitoring?
AI costs scale with usage. Without monitoring, surprises common. Budget alerts, cost-per-task tracking essential.
User feedback?
Critical signal. Thumbs up/down, satisfaction surveys, escalation rates. Quantitative metrics complement quality metrics.
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