Observability in AI Pipelines: Why “The System Is Up” Means Nothing

Monitoring vs Observability Observability is a term used widely in current systems, but it is often confused with monitoring. Monitoring tells developers whether something is not working or a flow is broken, whereas observability explains why a particular component within the pipeline is failing or malfunctioning. In most traditional applications, developers often monitor & track metrics around uptime, latency, error rates, CPU Usage, and memory. If the application API responds within the expected time and error rates stay within the limits, the application or system is considered healthy. If there is any deviation from the acceptable limits for any of these metrics, an email is triggered to the concerned team. Such a setup works for most of the systems.