Observability definition
Observability is the ability to understand what is happening inside a software system by examining the data it produces, mainly metrics, logs and traces. A highly observable system lets engineers ask new questions about unexpected behavior, such as why checkout is slow in one region, and find the cause without shipping new code just to investigate.
Metrics, logs and traces
Each signal answers a different question. Metrics show that something is wrong, logs explain what a component was doing, and traces show where in a chain of services the time or the error came from. Modern platforms correlate them, so an engineer can jump from a latency spike on a dashboard to the slow traces behind it and the log lines for those exact requests.
- Metrics: numeric measurements over time, such as request rate, error rate, latency and CPU.
- Logs: timestamped records of events, ideally structured as JSON with request IDs.
- Traces: the path of a single request across services, broken into timed spans.
- Profiles: continuous profiling that shows which code consumes CPU and memory.
- Events: deployments, configuration changes and feature flag flips that explain sudden shifts.
Observability vs monitoring
Monitoring watches for known problems: dashboards and alerts for conditions you predicted, such as disk full or error rate above a threshold. Observability is the broader property that lets you investigate problems you did not predict. In a monolith, a few dashboards might be enough. In a distributed system with dozens of services, queues and third-party APIs, failures take forms nobody anticipated, and rich, correlated telemetry is the only practical way to debug them.
Good alerting sits on top of observability. Alert on symptoms users feel, such as errors and slow responses on key journeys, ideally tied to service level objectives, and use the detailed telemetry to find causes once an alert fires. This keeps on-call engineers focused on what matters.
OpenTelemetry
OpenTelemetry, a Cloud Native Computing Foundation project, is the open standard for generating and collecting telemetry. It provides SDKs and automatic instrumentation for major languages, a Collector that receives, processes and routes data, and shared naming conventions for attributes such as HTTP routes and database calls. Because it is vendor-neutral, teams instrument code once and send the data to any backend, which avoids lock-in and makes switching tools far easier.
Observability tools and costs
Open-source stacks commonly combine Prometheus for metrics, Grafana for dashboards, Loki for logs and Tempo or Jaeger for traces. Commercial platforms such as Datadog, New Relic, Dynatrace, Honeycomb, Splunk and Elastic offer integrated experiences, and every cloud has native services such as Amazon CloudWatch, Azure Monitor and Google Cloud Observability.
Telemetry can become one of the largest infrastructure costs. Control it by sampling traces intelligently, dropping noisy debug logs in production, limiting high-cardinality metric labels such as user IDs, and keeping detailed data for a short period while retaining aggregates longer.
Example and how to implement
Users report a slow checkout. Metrics show higher latency for one region, traces reveal that the pricing service spends most of each request waiting on a database query, and logs for those traces show a missing index after a recent migration. The fix takes minutes once the evidence is clear. Nexzem implements observability with OpenTelemetry from the start of client projects, with SLO-based alerts and dashboards for each key user journey.