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Production monitoring should tell your team when users are having trouble, help locate the cause, and get an actionable alert to the people who can respond. Start with important user journeys and service objectives, then connect application, infrastructure, and user-experience telemetry to those outcomes. Metrics, logs, and traces are the core signals; dashboards and alerts are useful only when they help someone make a decision.
What should production monitoring tell you?
Monitoring is not just collecting data or checking whether servers are running. It should help your team answer three operational questions:
- Are users able to complete important tasks? Identify critical journeys and the availability, latency, and error behavior that matter to users and the business.
- Where is a problem occurring? Relate user-visible symptoms to the relevant application operation, runtime, or dependency.
- Who should act, and what should they do? Assign service ownership and route alerts into a response process with clear next steps.
AWS Well-Architected monitoring guidance asks, “How do you monitor your resources to verify they are performing?” Its operational guidance also stresses monitoring after implementation so teams can remediate issues before they affect customers. Treat that as an outcome-oriented responsibility, not a requirement to adopt a particular vendor.
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Choose a small set of meaningful indicators for critical operations. Depending on the application, those may include whether a user can sign in, complete a purchase, submit a form, or retrieve important data, and how long that action takes. Add technical measures that help explain those outcomes, such as request volume, faults, and resource health.
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For important operations, define service-level indicators (SLIs) and service-level objectives (SLOs): the measurements you use and the targets you intend to meet. Assign an owner who is responsible for responding when an objective is at risk. There is no universal threshold that fits every application; set targets and alert conditions from your service requirements and users’ expectations.
Which signals should you collect?
Metrics, logs, and traces answer different questions. Use them together, with consistent service and operation context, so responders can move from an alert to the relevant request and its supporting evidence. AWS Well-Architected describes these as the three primary pillars of observability.
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| Signal | Best for | Useful production questions |
|---|---|---|
| Metrics | Rates, levels, and trends over time | Are requests failing more often? Is latency rising? Has traffic changed? |
| Logs | Discrete events and their context | What happened during this failure? Which operation or error details were recorded? |
| Traces | A request’s path and timing across services or dependencies | Which part of a multi-step request is slow or failing? |
Make telemetry correlatable: use consistent service and operation names and include appropriate identifiers or context so a trace, related log events, and relevant metrics can be examined together. Avoid treating a single dashboard or an isolated exception as a complete diagnosis.
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Cover the application and the runtime
Instrument application behavior, including relevant application logs and metrics, and collect telemetry from the environment it runs on. Depending on the deployment, that may mean host, container, or platform signals. AWS Prescriptive Guidance describes operating-system-level logs and metrics alongside application logs and metrics as a minimum coverage approach; the setup varies with the compute environment. Application exceptions alone will not tell you whether a host, container, or platform issue is contributing to a user-visible failure.
Add direct evidence of user experience
Real-user monitoring (RUM) observes actual interactions, while synthetic transactions run checks against selected journeys. Use these where you need direct coverage of user-facing behavior or journey availability. They complement—not replace—application metrics, logs, and traces: a synthetic check can reveal that a journey is unavailable, while service telemetry can help explain why.
How do you implement monitoring?
Use the following sequence to build from service responsibility to actionable response. Adapt instrumentation details to the architecture and runtime you operate.
- Choose critical journeys and owners. Identify the user actions and service operations that matter most. Name the team responsible for each service and for responding when its objectives are at risk.
- Define indicators and objectives. Select measures that reflect whether those operations are working for users. Set SLO targets from your service requirements; do not copy a generic threshold without checking that it represents an unacceptable user or business outcome.
- Instrument the request path. Emit metrics, structured logs, and distributed traces for important operations. Standardize service and operation naming and shared context so the signals can be correlated during investigation.
- Instrument the runtime. Add relevant host, container, or platform telemetry alongside application signals. Choose collection and configuration that fit the actual compute environment.
- Check user-facing journeys. Add RUM when you need to understand actual user interactions, synthetic transactions when you need repeatable checks of important paths, or both when each answers a distinct question.
- Build dashboards and alert conditions around decisions. Give responders a view of availability, latency, traffic or call volume, faults, and errors for critical operations. Alert only when a condition warrants action and route it to the responsible team.
- Exercise and refine the response. Practice alerts and operating procedures in game days. After incidents, inspect the telemetry and response path, then improve indicators, instrumentation, or alert conditions as the service evolves.
How should dashboards and alerts work?
Use dashboards to orient, not to create more noise
Organize views around services and critical operations, so responders can see whether users are affected and which signals changed. Include the measures needed to assess availability, latency, traffic or call volume, faults, and errors. Where possible, make it straightforward to move from a service-level trend to a relevant trace or log context. A dashboard full of unrelated infrastructure measurements can obscure the decision a responder needs to make.
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Alert on conditions that call for action
Connect alert conditions to service objectives or meaningful operational thresholds. Decide who receives each alert and what response it is expected to trigger. Review false positives that interrupt responders without indicating a user-impacting or otherwise actionable condition, as well as missed incidents that should have been detected. A threshold is not useful merely because a monitoring tool makes it easy to configure.
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Use game days to verify that alerts arrive, the ownership is clear, and the documented response can be followed. Monitoring is part of an operational system: an alert that no one owns or cannot act on is not an effective safeguard.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How do you choose monitoring tools?
Compare tools against the architecture, signals, and response practices your team needs. Do not assume one product covers every runtime or workflow. Consider these questions before selecting a platform:
| Evaluation area | Questions to answer |
|---|---|
| Deployment compatibility | Does it support your runtimes and deployment model, including cloud, containers, serverless, on-premises systems, and your account structure? |
| Telemetry coverage | Can it collect the metrics, logs, and traces you need? Does it also provide the RUM or synthetic monitoring needed for your user journeys? |
| Correlation and diagnosis | Can responders search traces, inspect service dependencies, and connect requests with logs, metrics, or deployment context? |
| Objectives and alerting | Does it support the SLO workflow you need? Can your team define useful alert conditions and manage false positives? |
| Operational fit | Do access controls, data handling, retention, service ownership, and responder workflows fit your requirements? |
| Cost model | How do ingestion volume, retention, trace sampling, and feature-specific charges affect the cost for your usage? |
There is no neutral, current price comparison established here, so evaluate costs against your own expected telemetry volume and retention needs rather than assuming one category of platform is cheaper.
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AWS products as examples, not prerequisites
AWS documentation describes CloudWatch Application Signals as providing application metrics, traces, health views, and SLO tracking. AWS also describes OpenTelemetry as an optional collection approach. Amazon OpenSearch application monitoring presents service topology with RED metrics—Rate, Errors, and Duration. These are examples of AWS-documented capabilities, not independent head-to-head evaluations or a recommendation that every production application use AWS. Product capabilities, integrations, and availability can change, so verify current details against the deployment you operate.
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