Custom metrics are application- or business-specific measurements defined by engineering teams to track what actually matters to their systems and users. Unlike standard CPU, memory, or disk metrics, they capture domain events such as completed checkouts, failed logins, processed transactions, or model inference latency. They translate technical activity into signals aligned with business outcomes and service behavior.
How It Works
Teams instrument code to emit measurements at meaningful points in the application flow. This instrumentation typically uses client libraries from monitoring platforms such as Prometheus, OpenTelemetry, Datadog, or CloudWatch. Metrics are emitted as counters, gauges, or histograms and labeled with dimensions like region, tenant, endpoint, or feature flag.
Once collected, these signals flow through the same observability pipeline as infrastructure metrics. They are scraped or pushed to a backend, stored as time series data, and visualized in dashboards. Engineers define alerts based on thresholds, rate changes, or anomaly detection models applied to these domain-specific signals.
Because they reflect application semantics, they require collaboration between developers, SREs, and product teams. Careful naming conventions, label hygiene, and cardinality control are essential to avoid performance issues in the monitoring system.
Why It Matters
Infrastructure metrics show system health, but they do not reveal whether users can complete critical workflows. A service may run at low CPU utilization while silently failing to process payments. Domain-level measurements expose these gaps and enable alerting on real user impact.
They also support SLO design. Teams can define objectives around checkout success rate, job completion time, or API error ratio instead of generic host-level indicators. This alignment improves incident response, prioritization, and capacity planning.
Key Takeaway
Custom metrics turn application behavior and business events into measurable, actionable signals for operations and reliability teams.