In modern software development, the pursuit of optimal performance, seamless user experiences, and robust systems has led to the rise of advanced monitoring and analysis methodologies. Application performance monitoring (APM) and observability are two closely related approaches with key differences. Confusingly, the terms are sometimes used interchangeably. This post outlines the differences clearly.

APM is one component within an observability practice, providing dashboards and alerts for known or anticipated failures. Observability goes beyond simple monitoring. It embodies a holistic, dynamic practice, enabling teams to understand their complex systems and uncover solutions to potential issues, even those not yet on their radar.

What is APM?

APM is a tool designed to help teams monitor and optimize the performance of software applications. This type of monitoring ensures that applications are meeting performance expectations and delivering a positive user experience by comparing current conditions against known thresholds. APM provides dashboards and visibility into various aspects of an application’s behavior, such as latency, traffic, errors, and saturation. Whether those readings indicate good performance or bad performance is based on known or expected system failures. Alerts for engineers are triggered when those readings go above or below predefined numbers.

APM use cases span web applications, e-commerce platforms, mobile app performance, microservices architecture, and application releases and updates.

Benefits of monitoring

• Enables detection of pre-defined performance issues and errors 
• Works well for simpler application architectures where a small number of failure types are common
• A better user experience by optimizing application performance and minimizing disruptions
• Insights into resource consumption
• A common platform for development, operations, and other teams to collaborate, fostering a shared understanding of application behavior

Drawbacks of monitoring

• While APM systems are good at identifying aggregate issues like system-wide bottlenecks, they struggle to uncover granular, unknown, or unexpected issues that have not previously been encountered
• When monitoring applications in highly distributed and microservices-based architectures, interactions often span multiple services and components. Granular issues may be hiding within aggregated views or manifest as problems only noticed much further upstream, wasting time and effort when attempting to fix user experience issues.
• Limited scope, compared to broader observability solutions

What is observability?

Observability (sometimes abbreviated as o11y) is the ability to measure and understand any aspect of system or application behavior and performance. Modern software development teams quickly and methodically analyze telemetry data to discover and understand any problem within a service. It provides teams with a way to ask fact-finding questions of their data, pursue leads, and generally explore anything and everything that’s occurring when users interact with their applications.

Observability emphasizes collecting and correlating diverse data sources to gain a holistic understanding of a system’s behavior. First, teams start with a continuous cycle of data collection. Sometimes, this is summarized as needing “logs, metrics, and traces” in order to have observability. Technically, that’s true. Although having that type of data itself is just the start.

The context within that data is also important. The telemetry collected from your applications should contain as much rich context as possible. Good observability data should capture all kinds of details about what just occurred like transaction IDs, API endpoints, response codes, customer IDs, input parameters, durations, byte sizes, and just about anything else you think might be useful later to understand what was happening at any given moment. The more data you can capture, the better.

The telemetry you collect is only half of the story. What completes observability is your ability to analyze that data. In observability, the Core Analysis Loop is a method for asking questions, getting fast feedback, and determining whether you have the answer you need or what the next question should be. That type of ad-hoc analysis, and action, allows teams to quickly and effectively monitor, troubleshoot, and optimize their complex and distributed systems. Observability is the practice of continuously gathering various types of rich contextual data from your system and applications, so that you can understand their state at any given time through data analysis.

Observability use cases span microservice architectures, distributed systems, cloud-native environments, continuous deployment, incident response, and post-incident analysis. Observability can be used to debug any application performance issue.

Benefits of observability

• Extends beyond APM, incorporating a broad set of data sources and contextual information to provide holistic insights into system behavior and performance
• Can be used to analyze aggregate system-level application performance, individual requests, or anything with granularity in between
• Does not rely on defining hundreds or thousands of known thresholds for good performance or bad performance to detect issues
• Provides a comprehensive and real-time view of the entire software stack to offer insights into both expected and unexpected behaviors
• Granular visibility empowers teams to detect and immediately address issues before they impact the user experience
• Enables organizations to comprehend user experience within their applications, and make informed data-driven decisions on where to invest in delivering innovations or making reliability enhancements

Drawbacks of observability

• Like any new paradigm shift, thinking about problems in a new way and working with new tools means that there is a learning curve
• Once you see the value that adding custom data to your telemetry adds to your debugging experience and ability to understand any aspect of your applications and systems, you will want to add it more of it in more places and that process takes time

What are the differences between APM and observability?

To grasp the distinctions more concretely, let’s compare observability and APM along a few functional considerations.

Exploring the extensive reach of observability

Cloud-native architecture patterns have changed the design and operability of modern software systems. The distributed nature of modern cloud applications has improved resiliency and composability. But it has also introduced additional complexity. Now, engineering teams must often diagnose novel issues that have never been previously encountered (and will often never be seen again).

That shift in technology necessitates a more comprehensive approach. APM is a valuable tool for simpler architectures or monolithic applications with limited failure modes. Observability, which extends beyond APM capabilities, addresses a breadth of use cases where traditional monitoring falls short.

Observability is essential for gaining a comprehensive and dynamic understanding of modern software systems. By embracing observability, teams can navigate the complexities of distributed architectures, proactively identify and address any issue regardless of how novel or unknown, and ensure a positive user experience in an ever-changing and interconnected digital landscape.

Learn more

Honeycomb uniquely offers observability purpose-built to quickly surface anomalies and patterns across billions of requests in seconds, even when analyzing high-granularity data, where problems lurk behind any arbitrary combination of attributes. Honeycomb encourages its customers to add all the context and data they need in their telemetry, and to do the kinds of analysis engineers have long been told they can’t do because it’s too expensive or impractical, all without adding additional or unpredictable costs. In 2023, for the second year running, Gartner named Honeycomb as a leader in the Magic Quadrant for APM and observability.