Business success increasingly relies on digital tools, whose performance must always be guaranteed and in every context.
To ensure the smooth and continuous operation of IT systems, the first step is to gain a deep understanding of application performance in relation to user experience (UX). This allows quick intervention — or even proactive — in case of issues or critical problems of any kind.
To address this need, organizations turn to Application Performance Monitoring (APM) solutions.
Application performance monitoring: what it is and its link to UX
Application Performance Monitoring (APM) is, first and foremost, a type of IT systems monitoring. Specifically, it encompasses a set of practices and tools that collect, analyze, and correlate real-time application metrics—such as response times, throughput, error rates, and user interactions—to detect performance issues quickly, and enable proactive responses.
In this sense, APM is a process—enabled by dedicated tools and advanced platforms—that provides technical teams with all the information they need to ensure that applications, especially mission-critical ones, perform as expected without exception. For this to happen, and to quickly identify the root cause of subpar performance, application KPI monitoring must work hand in hand with other forms of monitoring, such as network monitoring, log monitoring, and infrastructure monitoring, to achieve a unified view.
A key element of the most widely accepted definitions is the connection between application performance and user experience. APM doesn’t just monitor back-end components—it also observes, from a performance perspective, how users interact with applications and how the resulting performance impacts their perceived experience. The goal, of course, is to optimize it continuously, under all conditions.
APM: the pillar of fast and high-performing applications
Businesses depend on increasingly complex and distributed applications that support critical business processes and continuous interactions with customers and partners. Relying on APM practices and tools is therefore essential for at least four reasons.
Monitoring application components is highly complex
Modern application architectures, built on microservices, containers, and multicloud environments, exponentially increase IT complexity. Every transaction, event, sensor, or device generates massive amounts of data. While necessary to meet user and business demands, this complexity complicates operations management and troubleshooting. APM gathers and analyzes data from multiple sources, simplifying application governance in the cloud era.
The rising cost of downtime
Application or service downtime leads to direct financial losses and often severe reputational damage. As dependency on digital systems grows, the cost of downtime has risen exponentially, affecting both large organizations and SMEs alike.
The Need for a Proactive Approach
Modern APM solutions go beyond detecting user-visible issues. They identify anomalies and deviations from performance baselines before they cause service disruptions. This proactive vision allows IT teams to act before any discontinuity occurs.
Direct Connection between UX and Business Performance
User experience has become one of the main differentiators for many companies, especially in digital and service industries. APM provides detailed insights into real user behavior (RUM, Real User Monitoring) and perceived performance, enabling continuous optimization of applications based on the needs of the target audience.
The difference between Application Performance Monitoring and Observability
It is not easy to clearly define the difference between Application Performance Monitoring and Observability. This is because APM originated as a specific operational process, while observability represents a broader, more modern approach to governing IT systems. What is certain, however, is that the two concepts are not synonymous today.
Two converging philosophies
APM was born out of the need to identify performance issues and inefficiencies quickly, and to ensure high service standards at the application level. It relies on indicators such as response times, error rates, service availability, traffic, and resource consumption, with the goal of keeping business-critical IT systems under control.
Observability, on the other hand, is an advanced approach to managing the complexity of cloud-native systems. It is not based on monitoring predefined metrics, but on the ability to understand how complex systems work by analyzing the outputs they generate. This enables teams to answer complex, unforeseen, and even entirely new questions. It marks a paradigm shift: from reactive control to proactive understanding, based on three data types: logs, traces, and metrics.
In practice, the theoretical distinction is becoming less rigid. Following a natural evolution, many APM tools today include features that enhance observability in the modern sense. It is therefore fair to consider APM as one of the building blocks of this new paradigm. In other words, while APM and observability are not synonymous today, they may become so tomorrow.
How Application Performance Monitoring really works
APM is not a single activity but a process that operates across multiple layers of the technology stack, capturing distinct signals and using different approaches to build a comprehensive picture that can be easily managed via centralized dashboards.
At the application level, APM solutions can measure countless parameters—such as average response times and error rates—and link them to code execution to pinpoint critical issues: a poorly performing API, a congested database, a latency-inducing function, orchestration bottlenecks, or an unstable external dependency.
These tools are also often applied at the infrastructure level, where they analyze anomalies in CPU, memory, or storage usage that may indicate imminent problems. As mentioned, once anomalies are detected, the tools must also support root cause analysis through in-depth subsystem examination. End-user monitoring—via both Real User Monitoring (RUM) and synthetic testing—also falls within the APM perimeter. Each of these disciplines is central to achieving effective governance of modern enterprise applications.
Given the wide range of signals and monitoring layers, as well as the complexity of enterprise application ecosystems, one of the risks is fragmentation across specialized tools. Next-generation APM platforms aim to eliminate tool sprawl by unifying monitoring into a centralized model, where automated correlation links alerts with their underlying causes.
Agent-based and agentless APM: two approaches to data collection
One of the key elements of APM platforms is the data collection mechanism, which follows two main approaches: agent-based and agentless.
In the agent-based model, small software components are installed on servers, virtual machines, and/or containers hosting the application. These agents can collect detailed real-time metrics, capture and forward system logs to the platform, and even run synthetic tests—automatic simulations of user actions to verify the availability and performance of critical services when no real users are active. This allows for highly granular and continuous data collection.
In the agentless model, monitoring is carried out without installing software, using approaches such as APIs, standard protocols, log parsing, or network sniffing. While this reduces invasiveness and simplifies management in dynamic environments (such as Kubernetes), it may limit the depth of available data. For this reason, hybrid approaches are often preferred in modern platforms.
The role of AI in Modern APM
Artificial intelligence is reshaping application monitoring, transforming the way anomalies are detected, root cause analysis is performed, and proactive responses to critical events are triggered.
In complex IT architectures, where dozens or hundreds of microservices interact in real time, identifying the source of an anomaly is no simple task, given the massive volumes of data generated. Machine learning algorithms applied to APM data can help IT teams by:
- Detecting anomalous behavioral patterns (anomaly detection);
- Predicting incidents through predictive analytics;
- Correlating events across different layers (applications, networks, databases);
- Prioritizing alerts based on their estimated impact on users.
This is not just about automation—it’s about enhancing IT teams’ decision-making capabilities in contexts that are already complex today and will become even more so in the future.
