What is APM (application performance management)?

A precursor to observability solutions, APM enables IT teams to visualize, prevent, predict and address application performance issues before they negatively impact users.

APM is also an acronym for application performance monitoring, wherein monitoring tools continuously collect data on application performance metrics such as response times, error rates, resource utilization and user activity. The terms are often used interchangeably; however, performance monitoring is just one component of a holistic application performance management strategy.

In addition to monitoring, APM uses data analysis processes (to identify trends, irregularities and performance bottlenecks), troubleshooting protocols (to automate root cause analysis and issue resolution) and optimization tools (to proactively address performance degradation and maximize app efficiency for users).

APM solutions can also help IT teams discern which apps and services are most essential to users and how performance issues might affect user productivity.

Effective APM tools, along with advanced observability solutions, can prove invaluable to organizations that rely on software applications to deliver services to end users.

APM tools help developers maintain the health and performance of enterprise applications, especially when the organization’s application portfolio is spread across IT environments (such as hybrid cloud and multicloud). Though the exact configuration of an APM system varies from business to business (and tool to tool), most leading APM tools operate within five key dimensions (originally published by Gartner Research).

Passive and active end-user experience monitoring tools assess how users are experiencing and interacting with an app. Passive monitoring refers to the continuous collection of user data (from sensors, network traffic and error logs) from actual users.

Active monitoring simulates user activity to better understand and predict situational software behavior (how an app might perform during an unexpected traffic spike, for example).

Experience monitoring tools within APM software can, for instance, run behavior scripts (or paths) to simulate customer experiences with the checkout process on an e-commerce site. Then, the software can monitor the scripts to understand how quickly the app processes payments and how it handles checkout throughput.

Runtime application architecture monitoring provides visibility into an application’s underlying architecture. It enables IT teams to understand how different app components and dependencies (such as databases, servers, network devices and virtual machines) interact to support the application.

APM tools automate the modeling process, dynamically mapping the topology of apps, services, infrastructure components and user interactions—ideally across on-premises data centers, private cloud, public cloud (including any SaaS solutions) and hybrid cloud environments. APM topology maps help IT teams quickly identify performance bottlenecks and optimization opportunities.

Also called business transaction management, transaction profiling offers a more specific approach to monitoring. Profiling features trace specific user transactions as they move through the application stack, from the user’s device and through every application component or resource involved in the transaction.

This monitoring approach gives developers granular insights into critical app features and functions, so they can address problematic or underperforming components before they impact the user experience.

DDCM zeros in on individual app components (such as web and application servers), monitoring specific code sections, external service calls, database queries and other elements at the code level.

Examining particular app components helps teams quickly identify the root cause of performance issues and make targeted improvements without involving unrelated components.

APM software collects massive amounts of data, and the analytics and reporting features within APM tools are central to the process of converting captured data into actionable insights.

APM platforms aggregate the data collected from each monitoring touchpoint to create digestible reports, dashboards and visualizations, enabling IT teams to identify performance trends and make informed decisions about solutions and optimizations.

Notably, Gartner Research has since collapsed its five dimensions into three. End-user experience monitoring is now called digital experience monitoring (DEM). The application discovery, tracing and diagnostics (ADTD) dimension encompasses three previously separate—but interlinked—functions: runtime architecture discovery, user-defined transaction profiling. The reporting and analytics dimension remains a stand-alone function.

Application performance monitoring tools focus exclusively on monitoring and represent only one aspect of APM.

Traditional monitoring solutions rely on small software components called “agents,” which are deployed throughout the application environment and supporting infrastructure to sample performance and performance-related metrics (or telemetry) at regular intervals (as frequently as once every minute). More modern solutions use agentless monitoring for a non-intrusive approach to data collection, relying on network traffic analysis to gather app performance data.

Application performance management is, in many ways, the next natural step in the app maintenance lifecycle (after monitoring). App performance management systems glean insights from application performance data and monitoring processes to help developers optimize the performance and availability of enterprise applications.

APM solutions provide one-size-fits-all tools for capturing rich data and analytics from services within an application. They make application architecture observable. And while the APM approach used to be sufficient for high-quality application management, it’s ill-equipped for the job of managing distributed apps and services with multiple runtimes and multiple layers.

Today’s applications rely on services and microservices, often running in containerized Kubernetes clusters. That means multiple runtimes with each runtime outputting logs in different locations within the architecture. To accommodate multiple runtimes using APM, developers would need to deploy multiple APM tools. They’d also need to use a log streaming service or another aggregation tool to consolidate logging data from each location. 

And as businesses add more services and microservices to the architecture, they introduce more complexity, making it harder to track requests when something goes wrong.

Observability solutions surpass APM tools by taking a holistic, cloud-native approach to application logging and monitoring. They provide seamless process automation and work with historical contextual data to help teams better optimize enterprise applications.

With observability tools, teams can better understand how services work with each other (using dependency graphs, for instance) and fit into the overall architecture. They can also use observability solutions to aggregate and vet app data and receive actionable insights from that data.

Today’s APM tools are versatile, with a range of customizable features that help businesses implement tailored APM strategies. Each feature can help IT teams get full-stack observability into their application ecosystems. Some examples include:

• Application monitoring: APM tools monitor the entire application stack, including the app framework (Java or .NET, for instance), operating system and database, any application programming interfaces (APIs) or middleware and the web application server.

• Error tracking: APM solutions can cluster related app errors into groups of issues, providing teams more context for understanding system errors and maximizing efficiency in the troubleshooting process.

• Infrastructure monitoring: With APM infrastructure monitoring protocols, teams can gather data on CPU utilization, disk space and network performance.

• Distributed tracing: APM tools enable developers to track data requests as they traverse the network, from front-end to backend services, helping teams monitor per-request dependences and isolate specific errors.

• Database monitoring: APM platforms help businesses visualize host-level and query performance metrics (such as top users, programs and objects), and assess SQL procedures to determine how database design and resource saturation affect app performance.

• Code profiling: Using APM tools, teams can take code performance snapshots to discover which app components are moving too slowly or using too many resources.

• Digital experience monitoring: APM tools can implement real user monitoring (RUM) and synthetic monitoring to gather performance data (load time, response time, latency, uptime and downtime) from user devices, bots and other non-human software components.

• Internet of Things (IoT) monitoring: Because users typically access apps using mobile devices, troubleshooting app issues can become a complex, time-consuming process. IoT monitoring capabilities within APM solutions provide teams a unified view of applications across connected devices.

Like many other IT operations (ITOps) practices and solutions, APM tools have changed significantly with the proliferation of artificial intelligence (AI) and the evolution of cloud computing.

The periodic sampling associated with traditional APM tools was sufficient for managing monolithic apps and traditional, distributed applications (where new code is released periodically and workflows, dependencies, servers and related resources are well-known or easy to trace).

But today, as businesses adopt modern application development practices and cloud-native technologies (such as Agile and DevOps methodologies, microservices, Docker containers, Kubernetes and serverless functions), they often deploy new app components too frequently, across too many languages and locations, to rely on traditional monitoring strategies. 

Furthermore, traditional APM techniques monitor code execution to diagnose issues. But today’s cloud-based SaaS applications comprise millions of lines of code, often spread across containers.

That’s why leading APM tools deploy cutting-edge monitoring instruments that enable full-stack observability, and rely on AI and machine learning (ML) technologies to correlate and analyze data in real time.

AI-driven APM tools can work across complex, distributed IT environments, deploying AI algorithms that can quickly analyze large volumes of performance data, correlate performance data with contextual data and pinpoint the root cause of performance problems.

Modern APM systems also use ML models to generate predictive analytics and forecast performance trends. And with natural language processing (NLP) capabilities, APM software can methodically sift through performance data and provide teams plain-language insights.

AI technologies aren’t without their challenges; explainability, privacy and data security are common concerns with AI-based IT tools. However, AI-driven APM software can significantly accelerate monitoring and troubleshooting and help enterprises make smarter, more proactive decisions about their application portfolios.

APM helps ensure that enterprise software applications stay efficient and reliable. They also facilitate:

Based on historical performance data, APM tools can forecast future resource needs, enabling more effective capacity planning and helping businesses scale their infrastructure as demand grows.

APM can facilitate continuous feedback through the software development lifecycle. Teams can monitor apps in both staging and production environments, helping developers establish a culture of continuous feedback.

SLAs dictate performance standards for most business applications, and APM services provide the data necessary for maintaining compliance with SLAs. Compliance metrics can also be used in stakeholder reports to demonstrate adherence to SLAs.

For applications that use external APIs, APM tools can track API response times and error rates so that organizations can identify issues with third-party services that may affect their application’s performance.

APM tools can help facilitate:

• Enhanced user experience. APM tools help businesses maintain high levels of user satisfaction and engagement, both of which are vital to customer retention and reputation management.
  
  
• Faster MTTD and MTTR. APM enables teams to find and fix performance issues and bottlenecks quickly, accelerating mean time to detect (how long it takes an organization to find an issue) and mean time to repair (how long it takes an organization to fix an issue).
  
  
• Better resource utilization. Monitoring and analyzing performance data allows organizations to optimize resource allocation, increasing cost savings and streamlining operations.
  
  
• Increased business agility. APM supports agile methodologies by providing real-time feedback on application performance, enabling rapid iteration and continuous improvement.
  
  
• Smoother migrations. Distributed tracing and service mapping features can help ensure that cloud migrations and modernizations go smoothly and don’t introduce errors to the network.
  
  
• Better employee collaboration. With APM’s end-to-end tracing capabilities, front-end and backend teams can access the same data simultaneously, facilitating more collaborative troubleshooting and optimization processes.