{"id":5877,"date":"2021-03-04T18:00:00","date_gmt":"2021-03-04T18:00:00","guid":{"rendered":"http:\/\/www.sumologic.com\/blog\/observability-vs-monitoring"},"modified":"2025-05-08T19:11:54","modified_gmt":"2025-05-09T03:11:54","slug":"observability-vs-monitoring","status":"publish","type":"blog","link":"https:\/\/www.sumologic.com\/blog\/observability-vs-monitoring","title":{"rendered":"Observability vs. monitoring: what’s the difference?"},"content":{"rendered":"\n
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One of the more delicate debates in the DevOps world is what observability has to do with monitoring.<\/p>\n\n\n\n

Is observability just a trendy buzzword that means the same thing as monitoring? Is observability an improved version of monitoring? Are monitoring and observability different types of processes that solve different problems?<\/p>\n\n\n\n

The answer to those questions depends in part on your perspective. Let\u2019s take a look at the different ways of thinking about observability and monitoring, and what they have to do with each other.<\/p>\n\n\n\n

Monitoring and Observability: A Brief History<\/h2>\n\n\n\n

Long before anyone was talking about observability, they were talking about monitoring. Modern software monitoring tools, like Nagios and Zabbix, trace their origins to the late 1990s or early 2000s. That\u2019s when IT teams first began deploying tools that systematically monitored application environments for problems and sent alerts when something seemed amiss.<\/p>\n\n\n\n

Monitoring tools have grown more sophisticated since then. Instead of merely pinging servers to see whether they respond, or tracking CPU and memory usage to see if they are maxed out, modern monitoring tools use sophisticated analytics to identify problems within complex software environments. Nonetheless, the core foundation of monitoring remains the same: it\u2019s all about finding problems.<\/p>\n\n\n\n

Observability is a much newer term, at least within the context of software. Although you can find occasional references<\/a> to the observability concept from the earlier 2010s, it wasn’t until circa 2016 that observability came into vogue among Site Reliability Engineers (SREs) and DevOps teams, as The New Stack explains<\/a>.<\/p>\n\n\n\n

Probably because the term is so new, observability is harder to define in a clean way than monitoring. Different people and companies offer different definitions. Most definitions of observability, however, focus on the idea that observability means collecting actionable<\/em> data in order to gain a true understanding<\/em> of problems identified by monitoring tools.<\/p>\n\n\n\n

Similarities between Observability and Monitoring<\/h2>\n\n\n\n

At a high level, then, observability and monitoring share some key characteristics.<\/p>\n\n\n\n

For one, they both help support the overall reliability and performance of software environments. They help find and (in the case of observability, at least) fix problems.<\/p>\n\n\n\n

They are also similar in that they both leverage multiple data sources. Although monitoring tools may not perform the advanced data correlation and analytics of observability tools, and monitoring tools usually rely primarily on metrics, they can leverage other types of data points \u2013 including logs and traces \u2013 to collect data from a software environment. Observability solutions, too, leverage a variety of data sources to gain the depth of visibility necessary to understand complex problems.<\/p>\n\n\n\n

How Is Observability Different from Monitoring?<\/h2>\n\n\n\n

From that perspective, at least, the difference between monitoring and observability boils down to the end goal. Whereas monitoring focuses on finding problems, observability focuses on understanding and resolving them.<\/p>\n\n\n\n

To put this into context, generating an alert when a node fails in your Kubernetes cluster would be an example of monitoring. That\u2019s simple enough to do: you\u2019d run an agent on the node itself (or possibly within Kubernetes) that would monitor whether the node is responding, and trigger an alert if it\u2019s not.<\/p>\n\n\n\n

In contrast, observability of the node failure would entail collecting data from multiple sources \u2013 the node, the hypervisor hosting it (if it\u2019s a virtual machine), the various Kubernetes services that were interacting with the node, and the pods hosted on the node \u2013 and then correlating those data sources to determine why the node failed. Maybe it was hosting a pod that suffered a major crash and took the node with it. Maybe it had a kernel panic at the OS level. Maybe it was shut down by an IaaS provider because you didn\u2019t pay your bill. Whatever the root cause of the issue, monitoring alone wouldn\u2019t expose it, but observability would.<\/p>\n\n\n\n

Using Monitoring and Observability Together<\/h2>\n\n\n\n

In general, DevOps teams seem to agree that observability and monitoring are distinct types of operations that address different problems.<\/p>\n\n\n\n

Still, monitoring and observability are inseparably linked. Monitoring tools can tell you when something goes wrong, and observability tools can help you investigate the issue after you detect a problem.<\/p>\n\n\n\n

Pairing monitoring and observability is beneficial because not all problems identified by monitoring tools require sophisticated investigation. Maybe your monitoring tools send an alert telling you that a server went offline, but it was part of a planned shutdown, for instance. In that case, you need not collect and interpret multiple types of data to understand what happened. You can just log the alert and move on.<\/p>\n\n\n\n

But when serious problems arise and you need to troubleshoot them quickly, observability data is crucial. Although you could technically collect the same type of data manually that observability tools deliver automatically, the data collection would take time and delay incident resolution. Observability tools ensure that you always have the data you need on hand to interpret a complex problem. Many solutions also offer recommendations or automated analyses that can help teams sift through complex observability information and identify root-cause problems more efficiently.<\/p>\n\n\n\n

Note, too, that a system must be observable \u2013 meaning that it must generate data that can be collected and interpreted \u2013 in order to be monitored. A closed system that offers no facilities for data collection (such as a proprietary IoT device that generates no metrics) can\u2019t be monitored for failure because it lacks observability. And, of course, it can\u2019t be observed, either, because there is no data to use as the foundation for observability.<\/p>\n\n\n\n

Is Observability Just a Buzzword?<\/h2>\n\n\n\n

You could argue that observability is a buzzword in the sense that teams were performing observability before the term existed. The idea of analyzing diverse data sources in order to understand complex application performance and reliability issues is hardly new. Indeed, platforms like Sumo Logic, which was founded in 2010, have been enabling this type of work for more than a decade \u2013 long before it became trendy to talk of observability.<\/p>\n\n\n\n

On the other hand, the buzz surrounding observability does seem to reflect some important changes that have taken place in the DevOps ecosystem in recent years. For one, the emergence of endlessly complex cloud-native environments has made the ability to collect and correlate complex data sets absolutely critical.<\/p>\n\n\n\n

When all of your applications ran as monoliths on virtual machines, simply monitoring them and then investigating issues manually may have been enough to keep everything running. In the cloud-native world of microservices, containers, serverless functions, and scale-out infrastructure, however, you need deeper visibility than you can achieve through monitoring alone.<\/p>\n\n\n\n

At the same time, the observability trend perhaps reflects increased awareness that DevOps practices should be tied to business goals. Monitoring helps technical teams identify technical problems, like a server that is down or an application that is not responding. Observability provides finer-tuned insights into application behavior and performance. It can help teams home in on a microservice that is causing problems for a particular subset of users, for example, or a resource allocation that is insufficient for handling application demand at certain times of day.<\/p>\n\n\n\n

In these ways, observability helps align technical issues with business issues. It makes it clear which business priorities (like serving a certain user group or handling a business event that leads to a spike in traffic) are impacted by technical shortcomings. In contrast, monitoring provides only high-level indications that something is wrong, without offering much opportunity for measuring the specific business impact of the problem.<\/p>\n\n\n\n

Conclusion: DevOps Teams Need Monitoring and Observability<\/h2>\n\n\n\n

You could go on and on in debating the precise similarities and differences between monitoring and observability, or arguing about whether observability is actually a useful term or a mere buzzword. But that\u2019s not going to improve the outcome of your DevOps processes.<\/p>\n\n\n\n

Instead, perhaps the best way to think about monitoring and observability is to approach them both as must-haves for modern DevOps teams. Yes, there may be some nuance surrounding how you implement them or exactly what each term means. But viewed from the perspective of DevOps operations as a whole, you need both monitoring and observability to find and fix application issues and, in turn, ensure an optimal end-user experience.
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