What Is Observability?

By Staff Contributor on February 10, 2023

In many organizations, it falls to three different teams to build, maintain, and troubleshoot problems within the complicated system of applications and hardware that keep networks functioning. IT professionals maintain and troubleshoot problems, development operations (DevOps) professionals integrate the processes between software development and IT teams, and site reliability engineers (SRE) help build reliable and scalable systems.

All of these teams have in common that they need full overviews of their domains to innovate, diagnose problems, and ultimately keep systems running optimally for the best end-user experience possible. That is where observability comes into play.

Simply put, observability uses a system’s output to gauge its overall performance. And while observability relies partly on monitoring, observability and monitoring are not the same. To better help organizations put a beneficial process for observability in place, a wide range of observability software is available to help implement the three pillars of observability — logs, traces, and metrics. Below is a complete guide to observability that outlines the benefits for each team within an organization and lists some of the features of a good observability platform.

Here is your holistic guide to observability, no matter your organization’s size.

What Is Observability?

Observability is a term used in both IT and cloud computing. It refers to the practices by which a system’s current state is measured by carefully examining the system’s external outputs. These measurements are obtained using the data a system generates, usually by analyzing that data with the help of logs, metrics, and traces, among other measures.

In IT, observability is a relatively new concept, and it is often incorrectly dismissed as a buzzword meant to replace the idea of system monitoring, particularly application performance monitoring (APM). However, observability is actually the next frontier in APM. Building on APM collection methods, observability better suits the changing nature of cloud-native applications. But observability is also not intended to replace monitoring. Instead, the main goal of observability should be to enable more efficient monitoring and APM.

The idea of “observability” originated in control theory, a subset of engineering focused on automating control of dynamic systems. To visualize control theory in action, think of self-driving cars or even managing the flow of water using a network of dams. These processes are both based on feedback from systems. Similarly, as cloud-native systems become increasingly more complex, the causes of system failures, slowdowns, and anomalies have also become more challenging to identify. And as organizations adopt more microservices, these systems continue to grow even more complex.

Observability — which includes understanding the ways in which information moves through channels — makes IT professionals better able to control the paths that information takes to reach its final destination. For this reason, observability has become more important than ever before. And as teams have begun collecting and examining observability data, that data has become critical for entire organizations, not just IT. 

In cloud-native environments, observability focuses on software tools and practices around aggregating, correlating, and analyzing the streams of performance data from distributed applications and the hardware those applications run on. Observing this data helps teams monitor, troubleshoot, and debug applications more efficiently to ensure better performance that meets end-user expectations and fulfills service agreements. Beyond fulfilling these requirements, observability can also extend to organizations’ software and procedures to analyze cloud performance data.

Generally speaking, observability encompasses all the ways that organizations understand their systems’ internal performance based on those systems’ external outputs. Every hardware, software, cloud infrastructure component, container, open-source tool, and microservice within a modern, cloud-based environment creates a record of each activity performed. Observability relies on the telemetry, or collection, of these records to quickly and easily help teams identify performance problems and trace those problems to their root cause without conducting time-consuming tests or additional coding.

To facilitate observability, many organizations adopt tools designed to help identify problems and then analyze their significance to the network as a whole, as well as mapping, software development life cycles, application security, and end-user experiences. Focusing on observability in these areas helps create more functional networks.

What Data Is Needed to Deliver Observability?

Often, observability is discussed by focusing on three main areas: metrics, traces, and logs. These are known as the three pillars of observability. Here are the definitions of those pillars as commonly used by IT professionals.


Metrics can be used to detect performance problems within a system. They give a numerical value to system performance data measured over a set period of time. Metrics provide information about defined and measurable attributes of a system, often called service-level indicators (SLI).

And because metrics are represented by numerical values, which change with time, many IT teams like to represent metrics using graphs. The graphical format allows IT professionals to map metrics in order to see at a glance how systems perform over time. There are many observability tools that can automate the graphing process to aid visualization. Once metrics are recorded and graphed, observability tools can also provide customizable alerts triggered when values cross predetermined thresholds.


Traces are an incredibly valuable pillar because they are used to troubleshoot. Traces act as flags, marking the places within a system where problems occur. Traces assign a universally unique identifier to each piece of data that moves through a system. As data travels, traces travel with it, which helps IT professionals and DevOps teams to track the lifecycle of data as it passes through the microservice system.

Tracing is especially useful in systems where multiple components exist and data is passed between them before reaching its final destination, especially for systems that use stateless computing in which data is transmitted with no information about the sender or receiver being recorded by either. In stateless environments, tracking data sent to multiple services for processing can be difficult. Tracing makes it much easier to spot issues around data that does not reach its intended destination.

Beyond troubleshooting undelivered data, tracing also illuminates the path by which data has traveled, provides information about how long data takes to travel its path, and offers insights into the data’s architecture during each step of the transmission process. Using this information, IT, DevOps, and SRE teams can more easily spot bottlenecks within systems and debug problems that arise within data flows.


While metrics assign numerical values to data, and tracing follows data through the delivery process, logs help DevOps teams to identify the root causes of problems. Logs work by creating a detailed record of a problem when it occurs. They provide a timestamped record of problems that occur within the software, devices, and applications, complete with granular information about the event. Logging is specifically relevant to DevOps teams, as they create and implement code for logging using their own standards. However, logging is easy to utilize since most software libraries and languages provide built-in tools for creating logs.

There are two main types of logs. Plaintext or unstructured logs are composed of free-form strings that are read by humans. Structured logs use data formats such as JSON string format. Plaintext logs are common for prototyping systems and creating data mockups because they are easy to read and can be quickly created by DevOps teams working to build software. However, structured logs are preferred by most DevOps professionals for modern observability because data formats like JSON are more useful for gathering analytics.

Of the three pillars of observability, logs allow for the most granular analysis of problems. That is why logs are most often used to discover the root causes of problems across systems and to explain inconsistencies, unpredictability, or suboptimal performance within systems.

Additional Data

Beyond the three pillars of observability, DevOps teams should also consider other ways to measure performance, such as considering the “digital experience” of a system. To observe digital experience, teams need end-to-end visibility, from the end user’s view to the backend of a system. This bird’s-eye view of an entire system should be used to observe the live operations of a system but also simulated for tests that can help to predict how systems will behave in different scenarios.

It is also worth noting that the three pillars of observability must be considered together in context to create a complete picture of a system. Siloing the three pillars of observability, or focusing on one without considering the others, can lead to faulty troubleshooting and allow issues to go unnoticed until they become major problems. Considering each of these pillars in conjunction with one another allows DevOps teams to efficiently identify and analyze problems, leading to better performance and, ultimately, a better end-user experience.

When choosing full-stack observability software, it is important to consider all three pillars of observability and how an observability platform will work to create a comprehensive picture. Here are some of the most important features to look for:

  • Infrastructure monitoring: These tools offer deeper views into a system’s underlying environment, whether in the cloud or on-premises.
  • Log investigation: This tool helps to cross-reference event logs with metric trends and traced data.
  • Real user monitoring: RUM assists in monitoring the digital experience by providing analysis for timing, errors, and other real-time contextual information.

Application performance monitoring: This feature keeps a record of traces and analyzes them to facilitate easier troubleshooting.

Who Needs Observability Software?

As systems increase in complexity, many teams within an organization need full-service observability software. This includes IT teams, DevOps teams, and site reliability engineers. Below is a brief overview of each of these teams, along with a few reasons they need reliable observability platforms.

IT Teams

IT is an umbrella term that can be used to describe many different groups within an organization. However, different IT teams exist to perform various functions and tackle other projects. While IT professionals can be generalists with a broad range of knowledge about maintaining systems and troubleshooting problems, in bigger organizations, IT professionals and teams are often geared toward specific tasks. IT teams can be focused on many areas, including architecture, networking, infrastructure, security, software development, support, and business intelligence.

Observability plays a vital role in each of these areas. The three pillars of observability are critical to everything from understanding a network to solving help desk tickets. Adopting easy-to-use observability software is an important way that organizations can provide IT teams in all areas an overview of an entire network for easier troubleshooting and problem resolution.


DevOps is not so much a role as it is a system of best practices and established procedures that organizations use to build and facilitate applications and services. DevOps focuses on orienting and coordinating the deployment and development of software with an organization’s IT teams. DevOps teams often work side by side with IT teams to improve systems, troubleshoot problems, and focus on growing networks while facilitating seamless end-user experiences.  

DevOps need a clear, end-to-end picture of network operations to innovate new solutions and identify network pain points. Observability software enables DevOps teams to easily visualize networks while optimizing procedures to create the best end-user experience possible. 

Site reliability engineering (SRE) Teams

SRE focuses on applying software engineering principles across operations and infrastructure processes. Applying these principles enables organizations to build more reliable and scalable software systems.In addition to software engineering, SRE also focuses on making software more reliable in several key areas, including availability, performance, latency, efficiency, capacity, and incident response. The teams who ensure software reliability are called site reliability engineers.

Site reliability engineers need observability platforms to see how systems operate at a base level. By studying systems as a whole, SRE teams can better see areas in which systems are unreliable and focus on pinpointed weaknesses to continue building more efficient networks.

What Is Observability in DevOps?

Observability is especially important for DevOps, as it offers developers a more complete picture of an application’s internal state. Developers can continuously monitor applications with observability software for real-time access to information about faults in distributed production environments. Here are a few other benefits of observability specifically for DevOps teams:


Observability can help teams find and address problems more quickly since deeper visibility means that DevOps teams can instantly see when systems change. By getting alerts the second there are changes, teams can debug and fix issues as they arise while also monitoring the problems those changes may have caused.


Observability offers a critical overview of systems. As networks grow to include an ever-increasing number of applications, devices, and software, it can be difficult for developers to pinpoint specific problems as they arise quickly. Observability offers a real-time view of production systems and information about what systems looked like before recent deployments.


Investigating issues and debugging systems can be a complex process, and observability helps to simplify and streamline operations by allowing developers to see a request’s journey from start to finish. Additionally, relevant contextualized data can save developers time.

Fewer Meetings

Without reliable observability software, developers must track down information about systems through third-party companies or additional applications to find information about who was initially responsible for certain services or to discover historical data about what systems looked like before they were altered. Observability provides this information, reducing time spent in meetings obtaining critical information and leaving developers free to focus on innovation.


One of the main friction points for DevOps teams is monitoring systems to troubleshoot problems. Observability makes these pain points more efficient, decreasing the time teams spend troubleshooting. This capability also frees development teams to spend more time coming up with solutions for improving networks and end-user experiences.

What Are the Benefits of Observability for the Entire Organization?

Observability is not just beneficial to DevOps teams, however. Adopting observability software benefits the entire organization, from developers, IT, and SRE professionals right down to end users and customers. A good observability tool can create pathways to better customer service by giving engineers and developers a more complete understanding of increasingly complex networks featuring intersecting microservices and applications. Observability platforms provide tools for collecting, exploring, and correlating every type of telemetry data. They also offer the ability to set customized alerts to detect changes in baseline performance.

In addition, observability also frees teams to focus on innovations and ways to grow an organization’s network by saving the time that would otherwise be devoted to managing the complexities of systems. Further contributing to innovation and growth, observability software also makes it possible to delve into the performance of new builds since teams can see error rate spikes and rises in application latency more easily. That way, they can better debug and problem-solve before an application launches, avoiding downtime and improving end-user performance.

Here is a top-level guide to the benefits of observability:

  • Simplification: Observability helps to make sense of incredibly complex systems.
  • Singularity: With so much data, it can be difficult to observe individual components. Observability can offer insights into hidden problems.
  • Faster troubleshooting: A complete system view can help teams spot irregularities more quickly.
  • Increase productivity: Traditionally, much of a DevOps team’s time is spent chasing down historical data around applications and licenses. Observability means all that information is readily available without a lot of time-consuming meetings, which, in turn, increases productivity.
  • Decrease “alert fatigue”: IT professionals, DevOps, and SRE teams increasingly have to handle an overwhelming number of alerts, often from parts of a system that do not fall under their umbrella of responsibility. Observability platforms generally offer customizable alerts to send the right message to the right team member instead of blanket alerts that could go ignored.
  • Automation: Manually observing an entire system allows problems to fall through the cracks. Automating tasks via observability software leaves less room for error and more time for productivity and innovation.
  • Faster time to market: DevOps teams are focused on rolling out the best solutions as quickly as possible. Observability ensures that applications can be rolled out quickly and problem-free.
  • Improved end-user experience: The faster teams can troubleshoot problems and implement useful applications, the better the overall experience for end-users and customers.
  • Reduced costs: When productivity is up, and time-consuming tasks are automated, an organization saves money it would have otherwise spent solving problems and managing IT help tickets.
  • Better information: Most organizations rely on an ever-increasing number of applications and processes to keep systems running efficiently. Having all relevant information about each of these complex parts available instantly to IT, DevOps, and SRE professionals helps foster organization-wide communication and collaboration.

What Are the Differences Between Observability and Monitoring?

Legacy methods of monitoring applications are largely focused on solving predictable problems. And in today’s increasingly complex systems, often involving a host of different microservices, effectively debugging and diagnosing problems cannot be achieved by relying on those legacy methods alone.

Observability offers insight into the internal structures of these complicated systems by allowing professionals to monitor the system’s output. And because observability somewhat depends on monitoring, the two terms are frequently used interchangeably. However, observability and monitoring are not the same. Here are a few key differences.


As stated above, observability is primarily focused on deriving key insights about a system’s internal structure by closely observing that system’s output. That is where the three pillars of observability come into play. Using logs, traces, and metrics, along with other criteria such as observable digital experiences, teams can put together a map of a system’s baseline performance. When irregularities in that baseline are observed, teams can more quickly address the root causes of those irregularities, tracking them either to a specific application or navigating a complex microservice architecture to trace the cause by identifying the effects. Without observability, monitoring becomes impossible.

A few key questions that can be answered using observability:

  • When a request is made, which services does it move through?
  • Where are bottlenecks occurring along that route?
  • How does a system normally respond to requests?
  • How is it behaving differently as the problem occurs?
  • Where exactly did the request fail?
  • How did individual microservices process the request?

The answers to these questions, provided by observability, aid in the monitoring process.


While observability provides insights, monitoring collects and stores the information accumulated through careful observability. Observability is not meant to replace monitoring, as monitoring is critical for understanding long-term trends within a system and using those trends to build dashboards and create alerts based on historical data.

These dashboards and alerts let teams understand the health of applications, how applications are growing, and how they are being used across an organization. Traditionally, the problem with relying on monitoring alone is that system failures tend to be unpredictable without a reliable system for measuring outputs alongside carefully monitoring internal systems and processes.

Monitoring helps keep a record of internal systems and known problems for reference regarding overall performance, making monitoring tools an invaluable part of running functional microservice-based systems. When monitoring best practices are informed by actionable data, they provide an excellent broad overview of a system’s health. That is why monitoring and observability go hand-in-hand to focus on a system’s macro and micro components from the inside out.

Observability: The Next Step

This guide shows that observability is more than just a trendy buzzword. It is actually a powerful set of procedures for helping an organization run as efficiently as possible. In addition to adopting the best network monitoring tools available, all networks, especially those utilizing a complex system of applications and microservices, should consider observability software that provides a complete overview of a network’s output.

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