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Application performance monitoring (APM) uses software instrumentation and telemetry to track how an application behaves, spot performance problems, and help teams investigate their causes. It focuses on requests and transactions—not just whether a server is running—and commonly uses response times, throughput, errors, metrics, logs, and distributed traces.
What is application performance monitoring?
APM is the practice of using software tools and telemetry to monitor the performance and operational health of running applications. AWS defines it as using software tools and telemetry data, including distributed tracing, to monitor running applications; Google Cloud describes it as observing and managing their operational health. These are provider descriptions, not a single standards-body definition.
The practical aim is to answer questions such as: Which requests are slow? Where did an error begin? Which service or dependency may be adding latency? APM connects application behavior to evidence that can help an engineering or operations team investigate those questions.
What does APM monitor?
APM commonly brings together several kinds of telemetry. The specific signals available vary by product and implementation; no single checklist applies to every platform.
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| Signal | What it tells you |
|---|---|
| Response time or latency | How long requests or transactions take. |
| Throughput | How many requests or transactions the application handles over time. |
| Error rate | How often requests fail or return errors. |
| Distributed traces | How an individual request travels through services and dependencies. |
| Logs | Event-level records that can add detail during an investigation. |
| Application metrics | Measurements that summarize application behavior over time. |
| Infrastructure signals | Resource use and system health that provide context for application behavior. |
CPU and memory readings can help explain an application problem, but infrastructure health alone may not show which user-facing operation is slow. APM adds application-level context about requests, transactions, services, and dependencies.
How does application performance monitoring work?
- Instrument the application. An agent, SDK, or compatible telemetry pipeline gathers data from the running application. The setup depends on the language, framework, and platform.
- Collect and send telemetry. The instrumentation produces signals such as metrics and traces, which are sent to a monitoring platform. Google Cloud documentation identifies OpenTelemetry and Managed Service for Prometheus as options for application-generated metrics and traces with application labels.
- Explore the data. Dashboards and searches can reveal changes in latency, throughput, or errors. Metrics show how behavior changes over time; logs record events; traces show the path of a request across services.
- Investigate and respond. Correlating signals can help narrow a slowdown or failure to an affected transaction, service, or dependency. Teams still need appropriate instrumentation, useful alerts and service objectives, and a process for investigating and fixing problems.
Collection, correlation, and alerting capabilities depend on the implementation and platform. APM can help detect and diagnose issues; it does not by itself prevent or fix them.
How is APM different from 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 provider descriptions do not establish a precise, vendor-neutral boundary. In practice, check what a particular team or product includes rather than assuming one universally agreed definition.
What to consider when choosing an APM platform
Compare platforms against your application and operational needs, not just the category label. Useful evaluation areas include:
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- Supported languages, frameworks, runtimes, and deployment environments.
- Instrumentation choices, including vendor agents and OpenTelemetry compatibility.
- Whether metrics, logs, traces, errors, and deployment events can be connected during an investigation.
- Visibility into services, databases, and other dependencies.
- Browser, mobile, or synthetic monitoring, if your application requires it.
- Alerting, service-level objective support, access controls, data retention, and data handling.
- Pricing model and controls for data volume.
For example, New Relic’s product overview describes distributed tracing, service maps, golden metrics, an error inbox, deployment context, and agent or OpenTelemetry instrumentation as features of its APM offering. These are the vendor’s product claims, not an independent comparison. Verify current feature availability and pricing directly with any provider you evaluate.
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