Application performance monitoring (APM) uses software instrumentation and telemetry to track how an application behaves, detect performance problems, and help teams investigate their causes. It looks beyond whether a server is healthy to show how requests, transactions, services, and dependencies affect application performance.
What is application performance monitoring?
Application performance monitoring is the practice of collecting and analyzing data about running applications so teams can observe their operational health and diagnose issues. AWS defines APM as “the process of using software tools and telemetry data, including distributed tracing, to monitor the performance of running applications” (AWS: What is application performance monitoring?). Google Cloud describes it as “the process of using software tools and telemetry data to observe and manage the operational health of applications” (Google Cloud: What is application performance monitoring?).
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These are source-specific explanations rather than a single standards-body definition. In practical terms, APM helps answer questions such as which requests are slow, where an error began, and whether a service or dependency is contributing to latency.
What does APM monitor?
APM tools commonly collect several kinds of application and system signals. The exact set depends on the instrumentation and platform; no one checklist applies to every product.
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- Latency or response time: how long a request or transaction takes.
- Throughput: how many requests or transactions the application handles over time.
- Error rates: how often requests fail or return errors.
- Distributed traces: the path a request takes across application services and dependencies.
- Application metrics and logs: measurements and event-level records that add detail during an investigation.
- Infrastructure signals: resource use and machine health, which can provide context for application behavior.
Infrastructure measurements such as CPU and memory can help explain a problem, but alone they do not identify which user-facing operation is slow.
How does application performance monitoring work?
A typical APM workflow starts by instrumenting an application, then collecting its telemetry and sending it to a monitoring platform. Teams use dashboards, searches, traces, and alerts to spot changes and investigate the affected behavior. Setup varies by programming language, framework, and deployment environment.
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- Instrument the application. An agent, software development kit (SDK), or compatible telemetry pipeline gathers application-level data. Google Cloud documentation identifies OpenTelemetry and Managed Service for Prometheus as options for application-generated metrics and traces with application labels (Google Cloud: OpenTelemetry instrumentation for Managed Service for Prometheus).
- Collect and send telemetry. The instrumentation passes supported signals to a monitoring system. Which signals are collected and how they are connected depends on the implementation.
- Detect a change. Metrics can reveal shifts in latency, throughput, or errors. Alerts can notify a team when configured conditions are met.
- Investigate the cause. A trace can show a request’s route across services; related logs and metrics can help pinpoint the affected event or component.
- Remediate and verify. APM provides evidence for diagnosis, but teams still need an investigation and repair process, useful alerts, and appropriate service objectives. Monitoring does not guarantee that an application will meet its performance goals.
Metrics, logs, and traces: what each signal tells you
These signals complement one another, but they are not interchangeable.
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- Logs record events and can provide details about what happened at a particular point in the application.
- Traces follow an individual request across services and dependencies, helping show where time was spent or where a failure occurred.
Correlating signals can help move an investigation from “requests are getting slower” to the specific transaction and service involved. The level of correlation available depends on how telemetry is collected and on platform capabilities.
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APM and infrastructure monitoring
Infrastructure monitoring focuses on the health and resource use of the underlying environment, such as servers or other compute resources. APM focuses on application behavior: the requests and transactions people or other systems depend on, and how application components and dependencies affect them. Infrastructure data can explain application symptoms, but it does not replace application-level instrumentation.
APM and observability
APM is commonly used for a defined application-monitoring practice, while observability is often used as a broader product or engineering framing. The terms overlap, and the available descriptions do not establish a precise, vendor-neutral boundary that applies in every context. Check what a particular platform actually instruments and connects rather than assuming its label guarantees a specific scope.
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What to consider when choosing an APM platform
Platform fit depends on the application and the questions a team needs to answer. Compare capabilities against the actual languages, infrastructure, and workflows in use.
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- Application coverage: supported languages, frameworks, runtimes, and deployment environments.
- Instrumentation options: available vendor agents and compatibility with OpenTelemetry.
- Signal correlation: whether metrics, logs, traces, errors, and deployment events can be connected for investigation.
- Dependency visibility: how the platform represents services, databases, and other dependencies.
- User-facing monitoring: whether browser, mobile, or synthetic monitoring is needed for the application.
- Operational controls: alerting, service-level objective support, access controls, data retention, and data handling.
- Cost controls: pricing model and ways to manage telemetry volume. Current prices are not established by the cited sources, so confirm them directly with each provider.
As one vendor-specific example, New Relic describes its APM offering as including distributed tracing, service maps, golden metrics, an error inbox, deployment context, and agent or OpenTelemetry instrumentation. These are the provider’s product claims, not an independent comparison or assessment (New Relic: Application performance monitoring).
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