Guide
Logs vs metrics vs traces
How logs, metrics, and traces differ, when to use each signal, and why production teams need all three for reliable incident detection and response.
Key takeaways
- Metrics show trends and thresholds across time.
- Logs preserve event-level detail for investigation.
- Traces connect work across services and dependencies.
Metrics show shape
Metrics are numeric measurements captured over time. They are the fastest way to see whether a system is healthy at a glance. Request rate, error rate, latency p95, CPU, memory, and queue depth are common operational metrics.
Metrics are ideal for dashboards and threshold alerts because they are compact and easy to aggregate. They tell responders what changed and how much it changed, but they often need logs or traces to explain why.
Logs show detail
Logs are timestamped records of events inside a service. They can include severity, message text, request identifiers, attributes, exception details, deployment markers, and configuration changes.
Logs are especially valuable when an engineer needs concrete evidence. A metric can show error rate drift, while logs can reveal which endpoint, customer path, or dependency emitted the failures.
Traces show path
Traces follow a request or unit of work across services. They help teams see where time is spent, which dependency was called, and how a failure moved through a distributed system.
OpenTelemetry gives teams a common way to produce logs, metrics, and traces with shared context. That context is what makes cross-signal investigation possible during incident response.
The strongest signal is connected
No single telemetry type is enough. Metrics show the trend, logs show the event detail, and traces show the service path. Incident management improves when these signals connect to alerts, ownership, and change history.
Driftdog is structured around that connected view: service, environment, severity, timestamp, and operational context travel with the evidence.