What Is Observability? Logs, Metrics, Traces Explained

Observability is the ability to understand what's happening inside a system by looking at what it produces on the outside — logs, metrics, and traces. A system is observable when you can answer "why is this broken?" without deploying new code or adding new instrumentation. The term comes from control theory, where it describes whether the internal state of a system can be inferred from its outputs.

In software operations, observability is built on three pillars. Metrics are numeric measurements over time — CPU usage, request latency, error rates, queue depth. Logs are timestamped records of discrete events — a failed database query, a user login, a deployment. Traces follow a single request through multiple services, showing where time was spent and where failures occurred. Each pillar answers a different question: metrics tell you something is wrong, logs tell you what happened, and traces tell you where in the chain it broke.

Observability vs. monitoring

Monitoring and observability overlap but aren't the same thing. Monitoring answers known questions: "is the website up?" "did the cron job run?" "is the SSL certificate expiring soon?" You define the checks in advance, and the system alerts when thresholds are crossed. Observability answers unknown questions — the ones you couldn't predict before the incident. A service is slow, but only for users in one region, only on POST requests, only when the payload exceeds 2 MB. Monitoring tells you it's slow. Observability tools let you slice the data until you find out why.

Most teams need both. Uptime monitoring and cron job monitoring catch the known failure modes — site down, job missed, certificate expiring. Observability platforms (Datadog, Grafana, Honeycomb) handle the investigation when something fails in a way nobody anticipated. They complement each other rather than compete.

Related terms: uptime, health check, MTTR, SLA