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Application performance monitoring is a technology-driven process to ensure business applications perform as expected through real-time performance tracking and trend analysis. In APM initiatives, IT professionals use monitoring tools and telemetry data to help detect, identify and troubleshoot performance issues in applications.
APM falls under the more general practice of application performance management. While the two might sound like the same process, they have different scopes and goals. Application performance monitoring focuses narrowly on tracking and improving the performance of individual applications to address problems as they arise. Application performance management encompasses that but also looks more broadly at optimizing application performance levels and health over time. In essence, performance monitoring is a key part of performance management.
An effective application performance monitoring program should track the processing of transactions and other types of user interactions, as well as the user experience (UX) and the status of application and IT infrastructure components. APM tools provide developers, application analysts, system administrators and IT operations workers with the data they need to resolve problems that affect an application's performance. The data collected by the tools includes various performance metrics that teams can use to analyze the root cause of performance problems and take corrective actions.
This comprehensive guide to application performance monitoring further explains what APM is, why it's important and how it works. It also covers the benefits of effective APM efforts, challenges to be aware of, best practices for APM practitioners, available APM tools and more. Throughout the guide, hyperlinks point to related articles that provide more information and expert advice.
APM proactively monitors the performance and behavior of applications and the systems they run on. The goal is to quickly find and fix performance issues -- ideally, before an application's users are affected to any noticeable degree. APM also helps development teams reduce the mean time to repair software defects that cause application errors or failures.
Without APM tools and processes, identifying and correcting application performance problems would be much more daunting. In modern IT environments, applications often involve numerous microservices -- dozens or more in some cases -- running across distributed systems. Each service handles a portion of the processing workload. The use of containers, different APIs and various cloud services, including third-party ones, further increases application complexity.
It's an arduous task to manually pinpoint the cause of slowdowns or errors in such applications -- for example, by reading through an application log. APM streamlines things by collecting various types of performance data for easy access by developers, application analysts and other IT pros. In addition to monitoring real-time performance status, it supports analysis of retained historical data to identify performance trends or hidden factors that are contributing to issues.
Ensuring that applications work well is a must for any organization. Uninterrupted application availability and efficient performance are essential to keep business processes running smoothly. Successful APM efforts that help do so lead to positive business outcomes and enable companies to avoid potential negative ones. For example, strong application performance prevents business disruptions, enhances customer satisfaction and improves financial performance.
Even with APM, some performance issues are inevitable, especially in complex applications with large numbers of users. But APM's rapid detection and diagnosis capabilities help minimize the duration of performance glitches or, in the worst cases, application downtime. This reduces frustration levels for internal users and customers alike. It also limits damage to a company's brand and reputation.
In addition, troubleshooting and remediating applications cost money and take up valuable developer time. APM can bring direct savings in both of those areas, enabling organizations to focus more of their development budget and resources on new applications or added functionality.
Not surprisingly, monitoring functionality is the core element of the APM process. But various forms of monitoring can be done: APM tools gather data from almost anything that plays a role in an application's performance. After being collected, the various metrics are commonly displayed in data visualizations and dashboards for use in performance tracking and analysis.
In the early 2000s, consulting firm Gartner outlined five functional dimensions of application performance monitoring. Listed below, they're still widely cited as a conceptual framework for APM -- even though Gartner later revised its model.
This involves gathering data to understand how well an application is performing for end users and gauge potential performance problems. For example, APM might monitor a website's response times and flag ones that exceed a predefined threshold to alert developers about possible latency or application response issues. These activities can be done through end-user experience monitoring (EUEM) or the broader practice of digital experience monitoring (DEM), which measures all of a user's digital interactions across various channels, processes and devices.
As part of either approach, these are the two principal ways to track UX:
This aspect models the various hardware and software components that execute an application and the paths they use to communicate with each other. When a performance problem is detected, IT teams can analyze the application architecture to identify the issue's possible scope and help guide more granular monitoring work. Pattern recognition can also be used to anticipate the potential for future problems and plan for preemptive upgrades, such as more CPU or storage resources.
Also referred to as business transaction monitoring, this focuses on examining specific user interactions with an application to understand the conditions that led to a performance problem. Doing so helps organizations trace discrete performance events and pinpoint where they're occurring as user requests move across an application's various components, such as different microservices. It also shows whether performance efficiency is optimized at each step in the process.
This involves tracking all the components in an application architecture to evaluate what's causing identified performance problems. That includes in-depth monitoring and analysis of the application components and supporting IT infrastructure, such as application servers and other middleware, databases and network flows. Component monitoring provides a deeper understanding of an application's various elements and pathways.
This involves translating the performance data gathered from the above processes into actionable information that can be used to do the following:
APM tools commonly provide interactive data exploration and analytics capabilities. Increasingly, they support the use of AI and machine learning to automate the analysis of performance data and do advanced analytics, such as data mining or anomaly detection. Both prebuilt and customized dashboards can be used to visualize, share and report on performance data, identified issues and APM initiatives.
Application performance monitoring tools track how applications are performing and whether they're behaving as expected. If an application isn't functioning properly, performance data is analyzed to identify the source of the issue and perform root cause analysis. After the situation is understood, actions are taken to resolve the issue. In addition, the application environment can be corrected as needed to avoid recurrences or similar problems in other application components.
At the application level, APM tools measure average response times, error rates, uptime and other performance metrics. They also monitor code execution to detect possible bottlenecks during application functions, such as processing steps, database queries or API calls. At the infrastructure level, the tools look for sudden spikes in the use of system resources that might indicate performance problems. That's all in addition to the in-depth component monitoring done after issues are identified.
Monitoring can be done with or without the help of software agents. In agent-based monitoring, agents are installed in applications to collect performance data. What they collect is driven by commands and scripts in code that's added to the application -- a process called instrumentation. Agentless monitoring, on the other hand, uses standard communication protocols to collect network traffic data.
Agents typically can gather more detailed performance data on a wider range of metrics based on how they're instrumented, but they add processing and management overhead. Conversely, agentless monitoring requires fewer resources and can be deployed and scaled more easily, but its data collection capabilities are generally more limited. As a result, the choice between them depends on an organization's application environment, performance monitoring needs and budget.
AI and machine learning tools can also help in the APM process beyond analyzing performance data. For example, AI algorithms built into APM platforms can map interdependencies between the different components of complex applications and automatically detect and remediate performance issues. AI can also automate alerting, notification and issue escalation procedures.
Application performance monitoring and observability, which emerged more recently as an IT process, are sometimes seen as the same thing. On the surface, they share the same purpose: to measure performance and identify potential problems in applications and underlying systems. But they diverge in scope, as observability provides a more comprehensive view of application health and behavior.
Fundamentally, the difference between observability and APM lies in the amount of data collected and how it's processed and analyzed. Observability aims to gauge the internal state of applications and systems by collecting a broader set of telemetry data and supporting more sophisticated exploratory analytics. While traditional APM mainly focuses on performance metrics, observability also incorporates the following data types:
Metrics, traces and logs are often described as the three pillars of observability. In that sense, APM can be viewed as an element of observability. Gartner, for one, has adopted that view; its long-running annual Magic Quadrant report on APM tools was expanded to cover APM and observability ones in 2022 and then refocused in 2024 on observability platforms, most of them with APM capabilities included.
Distributed tracing is the process of capturing traces in applications. From a technical perspective, APM and tracing are separate processes. While APM provides an overview of application performance, distributed tracing gives developers a detailed view of how an application performs on specific user requests that pass through various services and other components of a distributed system. Traces are broken down into spans, which represent the individual units of work done by different services. Treelike or waterfall diagrams are commonly used to visualize traces, with each span appearing in a separate bar.
When developers troubleshoot a performance issue, the average processing time recorded for each span in a trace can be used to identify which service is slowing things down or causing application errors. However, APM and distributed tracing are often used together, in keeping with metrics and traces being two of observability's pillars. While distributed tracing tools initially were a standalone technology category, many APM platforms now provide tracing capabilities.
There's a similar dynamic between distributed tracing and logging. Text-based application logs provide a chronological list of events with timestamps, status information, error codes and other data. Log files offer clues for debugging performance issues but don't readily show the chain of processing steps that traces do. As a result, tracing and logging complement one another and can both be used alongside APM.
Many metrics can be used to measure the performance and status of an application. The following are some common application performance metrics tracked as part of APM initiatives:
Effective APM strategies offer various business benefits to organizations, including the following:
APM provides additional technical benefits, such as streamlined SLA compliance tracking and the ability to troubleshoot performance issues in a controlled environment. It also supports application lifecycle management programs that provide a structured approach to the software development process, from defining requirements for an application to deployment and ongoing maintenance.
However, APM also poses challenges for organizations. As mentioned previously, modern application architectures can be notoriously complex. They often involve numerous services in distributed systems across multiple locations, including the cloud and on-premises data centers. Cloud-native applications and the increasing use of AI in business applications add to the complexity. This makes it difficult to monitor application performance effectively without solid planning and oversight of APM programs.
The following are some specific challenges and potential missteps in the APM process:
APM initiatives break down traditional monitoring silos for seamless application tracking. That includes the following:
Developers can prioritize applications for monitoring based on how critical they are to the business. In addition, teams can create rules and select monitoring parameters to trigger alerts when a problem is detected or an application's performance deviates from an established baseline. In many APM tools, alerting rules can be dynamic, with conditions that vary based on application workloads or other factors.
The cloud introduces a host of additional dependencies into application performance, even when applications aren't based in the cloud themselves. Cloud application performance monitoring is a form of APM that tracks apps running in the cloud, including ones in private cloud or hybrid cloud deployments. For example, an APM platform will monitor for communication problems between a cloud application and other cloud services the application connects to. It also looks for latency issues between the application and end users.
Other types of monitoring that can be done include mobile application monitoring to track and optimize mobile app performance; microservices monitoring to provide insight into performance in microservices environments; and website performance monitoring that focuses on web applications.
Also, automated application or network load balancing that spreads traffic across different servers can fool IT teams into thinking that everything is working properly because the combined performance of the servers appears to be fine. In reality, the automated balancing could be masking issues that leave some servers carrying more of the load than others. APM can help avoid this issue by simultaneously tracking performance at all the servers to identify where problems are occurring.
Because APM can be complicated and demanding, it requires careful thought, comprehensive planning and regular review. The following best practices for APM initiatives can help businesses get the most from their investments in the process:
Organizations can choose from a wide range of APM tools. Some are dedicated to APM tasks, while others have been rebranded as more expansive observability platforms. In other cases, APM functionality has been built into enterprise IT monitoring tools with broader features.
The following is an alphabetical list of some popular tools compiled by Informa TechTarget editors based on research of the available offerings and Gartner's observability platforms Magic Quadrant report. Vendors are listed parenthetically if they aren't mentioned in product names:
Open source APM tools are also available; Apache SkyWalking, Scouter, SigNoz and Uptrace are some notable examples. In addition, many commercial and open source tools alike support OpenTelemetry, an open source observability framework. It provides APIs, SDKs and tools to simplify the process of collecting metrics, traces and logs and prevent vendor lock-in on tools.
Based on their needs, organizations can instead choose narrower deployments of end-user experience monitoring tools that focus on RUM and synthetic monitoring. Such tools -- alternatively branded as digital experience monitoring ones in some cases -- are separate product modules in many APM and observability platforms. They're also offered by vendors that specialize in EUEM and DEM.
Given all the potential choices between tools, organizations should invest considerable time and effort in feature evaluation as part of the technology selection process to be sure they get the required monitoring capabilities. Individual tools have strengths in different aspects of APM.
In general, though, an APM tool should be able to efficiently collect performance data at scale, monitor the full infrastructure stack that supports an application, streamline analysis of performance issues and identify connections between application performance and business outcomes.
The following are some specific features that APM tools typically provide:
Tools alone don't make a successful APM initiative. Business and technology leaders should consider the following questions before launching an APM program:
With these questions answered, a business can decide whether to move forward with an APM deployment. It's often best to start small with a single application, develop expertise and then systematically expand the APM practices.
Editor's note: This article was updated in April 2025 for timeliness and to add new information.
Craig Stedman is an industry editor at Informa TechTarget who creates in-depth packages of content on analytics, data management and other technology areas.
Stephen J. Bigelow, senior technology editor at Informa TechTarget, has more than 30 years of technical writing experience in the PC and technology industry.
22 Apr 2025