A useful AWS observability design does more than collect logs and draw dashboards. It connects infrastructure health, application behavior, user-facing symptoms, and distributed request paths so operators can move from “something is slow” to a defensible explanation of where the slowdown begins. Amazon CloudWatch, AWS X-Ray, and OpenTelemetry solve different parts of that problem. CloudWatch is the operational system for metrics, logs, alarms, dashboards, and increasingly application-centric telemetry. X-Ray remains the AWS trace analysis service. OpenTelemetry provides the instrumentation and collection standard that can feed AWS and non-AWS back ends.
These distinctions matter in the monitoring and logging objectives behind AWS DevOps Engineer – Professional DOP-C02, but they matter even more in production. A team that sends every signal into one place without defining how metrics, logs, and traces relate will still struggle during an incident. The architecture should start with the questions operators need to answer, then choose the telemetry path that makes those answers fast and repeatable.
Metrics answer how much, how often, and how healthy
Metrics are numerical time-series observations such as request count, latency, error rate, CPU utilization, queue depth, throttles, or business transactions per minute. They are compact enough to aggregate and alert on, which makes them the foundation for detecting conditions that need attention. CloudWatch receives native service metrics from AWS resources and can also store custom application metrics.
The design challenge is choosing dimensions and resolution carefully. A metric labeled by service and environment may be cheap and useful. A metric labeled by individual user ID, request ID, or an unbounded URL parameter can create extreme cardinality and cost while making dashboards harder to interpret. High-cardinality details usually belong in logs or traces, where they can be searched when needed rather than aggregated continuously.
Good operational metrics reflect user impact as well as infrastructure state. CPU can be normal while a dependency is timing out. A queue can be growing even though each worker is healthy. Request success rate, latency percentiles, saturation, and dependency errors generally tell a clearer story than resource utilization alone.
Logs preserve detailed evidence that metrics intentionally compress. Logs record discrete events with richer context: an application exception, authentication failure, configuration decision, deployment message, or structured business event. CloudWatch Logs can centralize logs from applications and AWS services, apply retention controls, support metric filters, and make data available for search and analysis.
The strongest log design is structured. Fields such as service, environment, severity, trace ID, request ID, deployment version, account, Region, and error class let operators query consistently instead of relying on free-form text. Structured logging also makes it easier to correlate a spike in an error metric with the exact application messages that produced it.
CloudTrail is different from ordinary application logging. It records AWS API and account activity rather than replacing application or system logs. The distinction between control-plane evidence and operational telemetry is explored in CloudTrail and CloudWatch monitoring. In practice, an incident may require both: CloudWatch to show the symptom and CloudTrail to show which administrative action changed the environment.
Traces reveal the path of a request across distributed services
A trace follows a request as it moves through application components and dependencies. Each span represents part of the work: an API handler, database call, message publish, downstream HTTP request, or other operation. Distributed tracing is especially useful when a single user transaction crosses several services and no individual component appears obviously broken.
A latency metric can tell you that an API is slow. A trace can show that most of the latency is spent waiting for a downstream inventory service, which is in turn blocked on a database call. The trace turns a broad symptom into a path-level hypothesis that can be verified with metrics and logs from the implicated component.
AWS X-Ray stores and analyzes distributed trace data and can present service relationships and request details. The important architectural point in 2026 is that X-Ray as a tracing service should be separated from the older X-Ray-specific instrumentation model. AWS now recommends OpenTelemetry-based instrumentation for new and migrating applications.
OpenTelemetry is the instrumentation layer, not a replacement for every backend
OpenTelemetry is an open standard and ecosystem for generating, collecting, and exporting telemetry such as traces, metrics, and logs. Applications can use OpenTelemetry SDKs or automatic instrumentation, while an OpenTelemetry Collector can receive, process, sample, enrich, and export telemetry to one or more destinations.
This makes instrumentation less tightly coupled to a single monitoring vendor. The same application can emit standard telemetry while the collector decides whether traces go to X-Ray, metrics go to CloudWatch, or selected signals also go to another observability system. AWS Distro for OpenTelemetry, commonly abbreviated ADOT, packages OpenTelemetry components and AWS integrations for this pattern.
That separation is valuable during platform evolution. Instrumentation often requires code, agent, or deployment changes, whereas changing an exporter or collector pipeline may be operationally easier. Standardized telemetry also helps when a distributed transaction crosses AWS workloads, on-premises services, or third-party platforms.
The X-Ray SDK and daemon are now legacy instrumentation choices
A current AWS design should account for the X-Ray SDK and daemon support transition. AWS placed the X-Ray SDKs and X-Ray daemon into maintenance mode on February 25, 2026 and recommends migrating application instrumentation to OpenTelemetry. Maintenance mode means those components are no longer the path for new feature development, even though X-Ray trace analysis remains part of the AWS observability stack.
For existing applications, this is a migration problem rather than a reason to discard tracing. Replace X-Ray-specific SDK instrumentation with OpenTelemetry or ADOT, and replace the standalone X-Ray daemon with a supported collector path such as the CloudWatch agent or OpenTelemetry Collector where appropriate. Preserve trace context across service boundaries so a migration does not break end-to-end correlation.
Teams should also avoid mixing instrumentation styles indefinitely without a plan. Temporary coexistence may be necessary, but the target architecture should have clear ownership for libraries, collectors, sampling policies, exporters, and version upgrades.
Application Signals can turn telemetry into service-level health
CloudWatch Application Signals adds an application-centric layer over metrics and traces. It can surface services and dependencies, track standard application indicators such as latency, faults, errors, and availability, and support service-level objectives. That shifts the operating conversation from “is this instance healthy?” toward “is the service meeting the behavior users depend on?”
Service-level objectives are useful because they force teams to define what healthy means. A 99.9% availability objective for a critical API creates a different alerting strategy from a batch job that can finish within a broad processing window. An SLO should influence alarm thresholds, escalation urgency, deployment gates, and how much error budget a team is willing to spend on change.
Application Signals does not eliminate the need for raw logs or traces. It provides a higher-level operating view, while detailed telemetry remains necessary for diagnosis. The best hierarchy is service objective first, then supporting metrics, then trace and log evidence when the objective is at risk.
Correlation depends on consistent identity across telemetry types. Observability becomes much more valuable when a metric alarm can lead directly to relevant traces and logs. That requires consistent identifiers and dimensions. Service name, environment, deployment version, Region, account, operation name, and trace or request identifiers should be propagated deliberately.
Deployment metadata is particularly useful. If latency increases immediately after version 2026.09.27 is released, operators should be able to filter traces and logs by that version and compare it with the previous one. Without release identity, the team may know that the problem began after a deployment but still lack an efficient way to isolate affected requests.
Correlation also needs time discipline. Clock skew, inconsistent timestamps, and different retention windows can make a simple timeline surprisingly hard to reconstruct. Standard timestamps, retention policies matched to investigation needs, and centralized access reduce the friction of cross-signal analysis.
Sampling, retention, and cardinality are architecture decisions
Collecting everything forever is rarely practical. Traces can be sampled, logs can use tiered retention, and metrics can use dimensions chosen for aggregation rather than uniqueness. The objective is not maximum telemetry volume; it is enough trustworthy evidence to detect, diagnose, and explain important behavior.
Sampling should preserve the events that matter most. A low baseline trace sample rate may be acceptable for healthy high-volume traffic, but errors, high-latency requests, or important transactions may deserve higher retention. The collector layer is useful because it can apply processing and routing before data reaches a backend.
Retention should follow operational and compliance needs. Recent high-resolution logs may be valuable for active diagnosis, while older data may need cheaper archival storage or shorter retention. Before increasing volume, teams should ask what decision the extra telemetry enables and how often that decision occurs.
Alarms should represent actionable service conditions
An alarm that fires whenever a single component crosses a noisy threshold creates fatigue rather than reliability. CloudWatch alarms are most useful when they represent conditions with a clear operational meaning and a known response. Composite alarms can combine underlying states so teams alert on broader service health instead of receiving several notifications for the same incident.
Deployment-aware alarm design is also important. A change window may cause expected transient signals, but simply muting monitoring removes protection at the moment risk is highest. Better patterns distinguish expected deployment behavior from genuine user impact and keep rollback signals active.
Every high-severity alarm should have an owner, a diagnostic path, and a decision boundary. If no one knows what action an alarm should trigger, the problem is not the dashboard layout. It is the observability contract.
Build the observability path from user symptom to root-cause evidence
A coherent AWS observability architecture starts with a user or business symptom, detects it through service-level metrics, narrows the affected operation with dimensions and traces, confirms the failing dependency with distributed telemetry, and then uses logs and audit evidence to explain the exact failure or change. CloudWatch provides the operational surface, X-Ray provides trace analysis, and OpenTelemetry provides the modern instrumentation and collection model that can connect the pieces.
The important 2026 design shift is to avoid treating X-Ray-specific SDKs as the future instrumentation standard. Keep X-Ray where it is useful as a trace backend, but build new instrumentation around OpenTelemetry and supported AWS collection paths. That approach gives teams better portability, cleaner correlation, and a telemetry architecture that can evolve without rewriting every application each time the observability platform changes.
The same evidence should survive beyond the immediate alert. Correlated release markers, traces, logs, and API history give a later incident post-mortem a defensible timeline instead of forcing the team to reconstruct cause from memory.