Start infrastructure monitoring by deciding which systems matter, collecting a small baseline of metrics and logs, and alerting only on conditions that call for action. Add traces when you need to follow requests across services; use profiles when you need to locate code-level resource consumption. The right first setup depends on your environment and on what your team can operate—not on a universal vendor ranking.
What infrastructure monitoring should tell you
Monitoring is useful when it helps answer practical questions: Is a service or resource healthy? What changed? Is the issue affecting users or another important outcome? What should the responder investigate next? A collection of charts without ownership, context, or an action path may show activity without helping anyone respond.
Begin with the hosts, cloud resources, clusters, and networked services that support your critical services. Note who owns each area and who responds when something goes wrong. Then choose a monitoring route that covers those systems and fits the team’s deployment and operating preferences.
Keep the initial scope deliberately small. Include the components whose failure or resource constraints could affect an important service, plus the telemetry needed to recognize and investigate those conditions. Expand coverage when an incident or regular review shows a real blind spot, rather than collecting everything simply because it is available.
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Choose the right telemetry for the question
Metrics, logs, traces, and profiles describe different aspects of a system. They complement one another, but they are not interchangeable.
| Signal | What it represents | Useful for |
|---|---|---|
| Metrics | Numerical measurements over time | Spotting trends, comparing current behavior with a baseline, and alerting on measurable conditions. |
| Logs | Contextual records of events | Understanding what a host or application recorded around a time of interest. |
| Traces | A request’s path and timing across services | Investigating where a distributed request spent time or encountered a problem. |
| Profiles | Code-level resource consumption | Investigating which parts of a program use resources when a code-level explanation is needed. |
A practical first baseline is resource metrics and relevant system or application logs. Metrics can indicate when a condition changed; logs can add event details. Add traces where requests cross services and a request path is important to diagnosis. Add profiling when the unresolved question concerns resource use inside the code. Grafana’s overview of [telemetry signals](https://grafana.com/docs/grafana-cloud/learn-and-build/telemetry-signals/get-started/quick-start/) describes these signal types and their uses.
Pick a collection and monitoring path
Two common starting approaches are a cloud-native managed service and an OpenTelemetry collector connected to a backend. They solve overlapping needs, but place different responsibilities on the team.
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| Approach | When to evaluate it | What to consider |
|---|---|---|
| Cloud-native managed service | Your estate is centered on one cloud and you want to start with its native monitoring capabilities. | Check resource coverage, supported signals, collection setup, alerting and investigation workflows, data handling, and the current cost for your configuration. |
| OpenTelemetry collector plus a chosen backend | You need a collector-oriented path across several services, or want to select collection and destination separately. | Account for collector deployment and maintenance as well as backend setup, signal correlation, access, retention, and total operating burden. |
AWS-centered environments
Amazon CloudWatch is a reasonable first option to evaluate when AWS is the main environment. AWS’s [CloudWatch getting-started guide](https://aws.amazon.com/cloudwatch/getting-started/) includes guidance for collecting metrics and logs from EC2 instances and on-premises servers. CloudWatch documents metrics, logs, alarms, dashboards, and OpenTelemetry support; its [metrics documentation](https://docs.aws.amazon.com/AmazonCloudWatch/latest/monitoring/working_with_metrics.html) explains the metric model. CloudWatch also documents native OTLP ingestion. These are capabilities to assess against your actual environment, not a claim that it is the best fit for every team.
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AWS’s [monitoring and observability decision guide](https://docs.aws.amazon.com/decision-guides/latest/decision-guides/monitoring-on-aws-how-to-choose.html) lays out CloudWatch, X-Ray, and AWS Distro for OpenTelemetry in the AWS landscape. Use it to understand the options relevant to an AWS setup, then check whether each covers the systems and workflows you need.
Several services or Kubernetes
OpenTelemetry provides a collector-oriented way to gather and send telemetry. Its [operations getting-started guide](https://opentelemetry.io/docs/getting-started/ops/) is aimed at production operators who want telemetry from several services without changing application code in that operations path. Its learning path includes collector setup and Kubernetes automation. A collector is not the monitoring destination by itself: decide where the data will be stored, explored, and used for alerts.
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Grafana documents infrastructure integrations and a managed observability platform. Its [infrastructure monitoring guide](https://grafana.com/docs/grafana-cloud/observe-and-act/monitor-infrastructure/) and [telemetry insights guide](https://grafana.com/docs/opentelemetry/insights/) are useful starting points when evaluating Grafana Cloud or Grafana’s role in an OpenTelemetry-based stack. A managed destination may reduce some platform-operating work, while still requiring decisions about collection, access, retention, and usage.
Compare options on the estate you actually run: environment and integration coverage; instrumentation and collector effort; how metrics, logs, and traces can be correlated; the dashboard and alert workflow; data access and retention; team operating burden; and current total cost for the expected volume. Product capabilities and prices can change, and the documentation linked here does not establish comparable vendor prices or a neutral ranking. Check each vendor’s current pricing and regional availability for your configuration before committing.
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- Inventory the critical scope. List the services and infrastructure components that matter, their owners, and the person or team responsible for responding. Note the service or resource outcomes that would warrant investigation.
- Collect a baseline. Start with host or cloud-resource metrics and the system or application logs that help explain changes. In AWS, review the CloudWatch getting-started material for EC2 and on-premises collection. For multiple services or Kubernetes, assess an OpenTelemetry collector path and a suitable backend.
- Check that collection works. Confirm that telemetry is arriving for the systems in scope and that it is usable by the people who will investigate it. A configured collector or agent is not proof that the intended resources are represented correctly.
- Make a small dashboard. Put the signals an operator needs to view together, organized around a service or investigation task rather than a wall of unrelated graphs. CloudWatch documents dashboards that combine metrics and logs; Grafana documents dashboards for OpenTelemetry data. Make sure a responder can identify the relevant resource and time window.
- Add a few actionable alerts. Start with conditions that warrant a human response and have a clear owner and next step. CloudWatch documents alarms as a core monitoring capability. Avoid copying universal numeric thresholds: an appropriate threshold depends on the workload, baseline, and impact you care about.
- Use investigations to decide what comes next. When a metric identifies a time or resource of interest, use logs for event context. Add traces if a request crosses services and the path or timing is unclear; consider profiles if you need to identify code-level resource use.
- Review coverage, workload, and cost. Check whether the chosen service covers the estate, whether the team can operate it, and how ingestion, retention, and queries affect the current cost for your actual volume. Revisit the scope when systems or service priorities change.
Design dashboards and alerts for response
A dashboard should support a decision or investigation. Group related signals so a responder can move from an affected service to the relevant resource and time window. Prefer a clear view of the initial baseline and the conditions that matter over a large catalogue of charts no one owns. The exact panels depend on the systems in scope; the sources here do not establish one universal dashboard layout.
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An alert is useful when someone needs to act, not merely because a metric moved. For each alert, decide who receives it, what condition it represents, why it matters, and what the first investigation step is. Keep the initial set small enough to review and maintain. If a notification repeatedly arrives without a useful action, revisit its purpose and routing. Do not borrow a numeric threshold from another workload without establishing that it represents a meaningful condition in yours.
Keep dashboards and alert rules connected to ownership. If nobody can identify the system, understand the signal, or take the next step, the monitoring setup is incomplete even if data is flowing.
Troubleshoot common first-setup problems
- No telemetry appears: Check that collection is configured for the intended host, service, or resource and that the selected path supports the signal you expect. For a collector design, verify both collection and forwarding to the chosen backend.
- Some systems are missing: Compare the actual inventory with the resources represented in the monitoring service. Revisit integration coverage and collection configuration; a dashboard cannot display data that is not being collected.
- A chart is hard to interpret: Make its resource and time context clear, and pair it with relevant logs or related signals where useful. If the chart does not help answer an operator question, remove or redesign it.
- Alerts do not prompt a useful response: Recheck the condition, owner, routing, and intended action. Adjust the rule to the workload’s observed behavior and service impact rather than using a generic threshold.
- An incident remains unexplained: Use the signal that matches the unanswered question. Metrics can locate a change, logs can provide event context, traces can show a cross-service request path, and profiles can investigate code-level resource use.
- Operating effort or cost grows unexpectedly: Review what is collected, where it is sent, and the backend’s current ingestion, retention, and query charges. The available product documentation does not provide a like-for-like cost comparison; estimate using your own expected volume and current vendor pricing.
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