Monitoring tells you something is wrong. Observability helps you figure out why. As systems become more distributed, the gap between those two grows, and closing it depends on combining three complementary signal types: metrics, logs and traces.
The three signal types are correlated around a shared identifier so an engineer can move from "something is wrong" to an insight and a response — rather than checking each signal in isolation.
Metrics are numeric measurements over time — request rate, error rate, latency, CPU usage — useful for detecting that something changed and for alerting. Logs are discrete, timestamped records of specific events, useful for understanding exactly what happened at a point in time. Traces follow a single request as it moves through multiple services, useful for understanding where time is spent and where a failure originated in a distributed system. Each answers a different question; systems that only invest in one tend to have painful blind spots during incidents.
Prometheus is a commonly used metrics collection system that scrapes numeric metrics from instrumented applications and infrastructure on a regular interval and stores them as time series. Grafana is typically paired with it (and with other data sources) to visualize those metrics as dashboards. On AWS, CloudWatch provides a similar role natively — collecting metrics and logs from managed services without requiring separate instrumentation for infrastructure-level signals.
Alerts should be based on symptoms that matter to users (elevated error rate, degraded latency, failed health checks) rather than every possible internal metric crossing a threshold. Alerting on causes instead of symptoms tends to produce noisy, low-signal alerts that teams eventually learn to ignore — which defeats the purpose of alerting in the first place.
A useful dashboard answers a specific operational question at a glance — "is this service healthy right now" — rather than displaying every available metric. Layering dashboards (a high-level system overview, then service-specific detail dashboards one click away) helps during an incident, when the priority is narrowing down where the problem lives as fast as possible.
The real value of having metrics, logs and traces together shows up during an incident: a metric shows latency spiked at a specific time, a trace shows which downstream service is responsible for the added latency, and logs from that service show the specific error or condition that caused it. Without a shared identifier (like a request ID or trace ID) connecting these signals, correlating them manually during an incident is slow and error-prone.
Beyond incidents, the same signals support ongoing performance analysis: identifying which endpoints are slow, which database queries dominate response time, and how performance trends as load grows. This turns performance work from guesswork into something based on actual measured behavior.
Good observability means an on-call engineer can go from "something is wrong" to "here's the specific cause" without needing to guess or add new instrumentation in the middle of an incident response. That requires the metrics, logs and traces to already be in place, connected, and reviewed regularly — not assembled for the first time under pressure.
Monitoring checks known conditions and alerts when thresholds are crossed. Observability is the broader ability to ask new questions of a system using its metrics, logs and traces — including about failures nobody anticipated.
When a single request passes through several services and you need to see where time is spent or where it fails. For a single monolithic application, metrics and logs usually cover most investigations.
Alert on user-facing symptoms — error rate, latency, availability — rather than every internal metric, make each alert actionable with a clear owner and runbook, and regularly remove alerts nobody acts on.
Observability
Modern systems need more than basic monitoring. Learn how metrics, logs and traces work together to provide useful operational visibility.
More on this and related topics.
A complete guide to Amazon CloudWatch Omni, AWS's AI-powered observability platform for applications and AI agents, built on OpenTelemetry.
AWS Agent Registry gives enterprises a centralized, governed catalog for discovering and approving AI agents, tools, skills and MCP servers — here is how it works and what it does not solve.
A practical guide to designing cloud infrastructure with the right balance of reliability, security, scalability and operational control.
