Reliability problems rarely announce themselves in one neat signal. They may first appear as a rise in failed requests, slower response times after a deployment, an unusual pattern in traces, or reports of an outage in Europe that prompt the team to check whether the issue is local, regional, or unrelated. Good monitoring brings those clues together through metrics, logs, traces, alerts, and a clear incident response process.
Start with service objectives
Begin with service level indicators, or SLIs. They describe what users experience: availability, successful requests, latency, and correct results. Then define service level objectives, or SLOs, for the reliability the service should deliver.
This is better than copying one threshold to every system. A checkout API and an internal batch job do not need the same latency target. Alert rules should reflect the service, traffic pattern, and business impact.
Track the error budget too. It shows how much unreliability the service can absorb before the SLO is at risk. Fast-burn and slow-burn alerts can catch sudden failures and longer degradation without paging on every small spike.
Monitor signals that explain user impact
Application monitoring should focus on signals that show how a service behaves from the user’s point of view. The most useful ones usually include:
- request volume and throughput;
- failed requests, especially 5xx errors;
- 4xx errors that may point to product or integration issues;
- latency at percentiles such as p95 or p99;
- availability and uptime checks;
- retries, timeouts, queue depth, and saturation;
- third-party API latency, errors, and availability.
These signals explain what is happening inside the application, but they do not always show the full picture. When an external dependency starts behaving unexpectedly, checking whether a similar failure is being reported elsewhere can add useful context before the team assumes the problem is local.
Latency also needs careful interpretation. An average response time may look healthy while a smaller group of users experiences much slower requests. Percentiles such as p95 and p99 make this tail latency easier to detect.
Infrastructure metrics remain important as well. CPU, memory, disk usage, container restarts, network behavior, and resource saturation can all help explain application degradation. However, a high CPU value alone should not automatically trigger an incident. It becomes meaningful when it affects users, reduces available capacity, or signals a broader reliability problem.
Use metrics, logs, and traces together
Metrics show that something changed. Logs add event details. Distributed traces show where time was spent across services and dependencies. Together, they speed up root-cause analysis.
OpenTelemetry provides a vendor-neutral way to collect traces, metrics, and logs. It makes it easier to change observability backends without redesigning instrumentation.
On Google Cloud, Cloud Monitoring and Cloud Logging provide native monitoring and alerting. Managed Service for Prometheus supports Prometheus and OpenTelemetry metrics, PromQL, Grafana, and hybrid environments without running the full Prometheus backend.
Design alerts for action, not noise
Every alert should answer three questions: what is wrong, who is affected, and what should the responder do next.
Prefer alerts tied to SLOs, error-budget burn, sustained latency, availability, or a clear failure condition. Use warning and critical levels only when they lead to different actions. Add a runbook, dashboard, service owner, and incident context.
Route urgent alerts to the on-call path and use a backup channel for important incidents. Non-urgent signals belong in dashboards, tickets, or review queues. Test alerts by simulating failures and checking firing and recovery notifications.
Build a repeatable incident workflow
When an alert fires, verify the signal first. Check user impact and related telemetry. Then look for recent releases, configuration changes, dependency failures, or capacity problems.
Mitigate before optimizing. Roll back a bad release, shift traffic, disable a failing feature, scale a constrained component, or fail over when supported. Escalation should depend on impact and risk, not only on a timer.
After recovery, record what happened and improve the system. Update the runbook, tune an alert, add missing telemetry, or remove a noisy rule.
Keep monitoring maintainable
Treat dashboards and alert policies as code. Remove dead alerts, control high-cardinality metrics, and watch telemetry costs. Automate only safe, tested actions.
The goal is not to collect more data. It is to detect user impact early, explain it quickly, and give the team a reliable path from alert to recovery.