Maintaining scalable and highly available apps is difficult for DevOps professionals. That's because these intricate, interconnected systems behave in unforeseen ways and problems frequently have hidden causes. Tools and methods that were effective when we created monolithic programs cannot handle the volume of data dispersed environments produce.

They don't consume enough data or give enough information about the app's condition to fix issues promptly. Fortunately, there are techniques and tools that deal with these problems. Observability and monitoring are essential when attempting to monitor, manage, and enhance complex microservice-based applications efficiently. Let's take a look at what those terms mean.

What is Observability

Observability is the ability to measure a system’s current state based on the data it generates, such as logs, metrics, and traces. It helps thoroughly understand a system by examining the inputs.

An observability platform enables IT operations teams to simultaneously monitor—or gain a deeper understanding of—the status and health of various apps and resources within your IT infrastructure. IT staff can proactively identify anomalies, examine problems, and find solutions by drawing insights from each system's data.

As cloud-native systems have grown more complicated and it has become harder to identify the potential root causes of a failure or anomaly, observability has become more crucial in recent years. Teams understand the benefits of observability data for the business and IT as they accumulate and use it.

The relationships between systems in your company's multi-layered IT infrastructure, including cloud environments, on-premises software, and third-party apps, are understood using observability tools, which employ algorithms based on the mathematical control theory. 

These tools then use logs, metrics, and traces—the so-called "three pillars of observability"—to monitor the condition and status of your systems. When the tool notices an anomaly, it alerts the team and gives them the information they require to troubleshoot and resolve the problem rapidly.

What is Monitoring

Monitoring is a procedure used to systematically gather, examine, and analyze data to actively monitor performance, maximize beneficial effects, and reduce the risk of negative effects. Because it can offer early and continuous information to help shape implementation before evaluations, it is a crucial component of efficient management.

As opposed to a single discrete evaluation event or even numerous linked discrete evaluation events, monitoring incorporates evaluative thought into the periodic collection, analysis, and use of information during implementation (such as a mid-term and final evaluation). This line has been blurred by more recent types of evaluation, like developmental evaluation and real-time evaluation, which involve ongoing data collecting, analysis, and use.

Monitoring systems frequently need to be incorporated into an organization's ongoing internal processes. Monitoring may become a more challenging managerial task than evaluation due to the integration.

Observability V/S Monitoring: What’s the Difference? 

Monitoring enables observability. DevOps frequently examines numerous indicators at once while monitoring applications to assess the health and performance of each one. Program monitoring in DevOps requires gathering and displaying data from many IT systems since it reveals when a system or application is having a problem. However, teams find it challenging to identify the underlying reason for the performance issue without observability.

Together, observability and monitoring solutions provide deep insight into the condition of your IT infrastructure. While monitoring warns the team of a potential problem, observability assists the team in identifying and resolving the problem's root cause. Monitoring an endpoint's performance is essential even when it cannot be observed directly since it provides additional data that can be used to identify and address any issues with the system.

Difference between observability vs monitoring explained

For development teams, the line between observability and monitoring is frequently hazy. In this article, we'll talk about observability vs. monitoring and their interrelationship.

Collection vs. Context

Monitoring notifies you if there is a fault in the system using a predetermined set of metrics and logs. You can identify a predetermined set of failure modes by monitoring applications. Monitoring technologies like application performance monitoring (APM) can inform you whether a system is online or offline or whether there is an issue with the performance of an application.

Observability tells you what the problem with a system is and how it was caused. It combines the information and data that monitoring generates to give you a comprehensive understanding of your system, including its performance and health.

Observability tells you why a system is at fault, and Monitoring notifies you that a system is at fault.

Tracking vs. Knowledge

Monitoring is the process of tracking a system’s performance over time. Metrics are used by monitoring systems to notify IT teams of operational issues with applications and cloud services. The main use of monitoring is alert teams if there are any issues with the system. 

The degree to which the system's internal states may be deduced from knowledge of its external outputs is measured by its observability. It combines the information and data that monitoring generates to give you a comprehensive understanding of your system, including its performance and health. Therefore, the degree to which your monitoring measures can decipher your system's performance indicators will influence its observability.

Observability acts as a knowledge base in defining what to monitor, and Monitoring focuses on monitoring the systems and discovering faults.

Data Collection vs. Data Interpretation 

In order to be observable, a complex environment must allow you to interpret its interior state from data you gather about its surface. It involves, for instance, figuring out that a downed server or memory exhaustion is to blame for your application's poor response time. Observability is driven by surface-level data, which can take many different shapes. They include data from complementary systems, such as CI/CD pipelines or help desks, which provide crucial context for data, as well as logs, traces, and metrics from the software and infrastructure used in the environment where applications are run.

Monitoring provides data on the usage trends and performance of a program. High availability is one objective of monitoring by reducing important time-based KPIs. Application and service monitoring depend on features like real-time streaming, historical replay, and visualizations. A well-monitored deployment provides data about its performance and health, enabling the team to identify production incidents quickly.

Observability focuses on giving context to the data, and Monitoring focuses on collecting data.

Key Criteria vs. Complete Assessment

Due to observability, cross-functional teams can better comprehend and respond to precise queries about what's happening in highly distributed systems. You can identify what is slow or broken and what needs to be done to improve performance, thanks to observability. Teams can receive alerts about issues and proactively fix them before they have an impact on users when they have an observability solution in place.

Unlike observability, in a monitoring scenario, you keep track of important KPIs that are intended to warn you of performance concerns you anticipate seeing later. Monitoring keeps track of all the KPIs, and you can see if there is any difference between real-time data and a predetermined value.  

Observability gives a more complete assessment of the overall environment, while Monitoring focuses on KPIs. 

Limited vs. Sustainable

A better product can be produced by using observability as a sustainable alternative to monitoring operational insights, improved incident management, and quality control. It aids in securing practical insights for quicker feedback loops and better decision-making. 

In the context of observability v/s monitoring, Monitoring is limited, while Observability is sustainable. 

Single Plane vs. Traversable Map

In monitoring, you focus on setting up key rules and KPIs that need to be tracked. This makes monitoring a single plane as it can only alert you if there is an issue, you cannot map out the origin of the problem. The key assumption in setting up monitoring is that you’re able to predict what kinds of problems you’ll encounter before they occur.

On the other hand, Observability shows you the problem and lets you understand why there is an error or issue. It lets you traverse the different scenarios and reach the case of the problem, making it easier for teams to understand and troubleshoot.  

Monitoring methods are single plane, while observability is a Traversable map.

Process vs. Potential

While the term "monitoring" is occasionally used to refer to anything distinct from observability, monitoring is a process that, combined with tracing and logging, makes a system observable. As the "three pillars of observability," monitoring, tracing, and logging are frequently referred to.

Understanding when anything goes wrong within the application delivery chain is crucial so you can find the underlying problem and fix it before it affects your business. A two-pronged strategy is provided by monitoring and observability. Situational awareness is provided via monitoring, and observability aids in determining what is happening and what should be done about it.

Observability creates the potential to monitor different events. Monitoring is the process of using observability.

Observability and Monitoring: Better Together  

DevOps teams appear to concur that monitoring and observability are separate operational kinds that solve different issues. Observability and monitoring go hand in hand. Monitoring tools can alert you when something goes wrong, and observability tools can support your investigation if you notice a problem.

Because not all issues found by monitoring technologies call for in-depth analysis, pairing monitoring and observability are advantageous. For example, your monitoring software may warn you that a server has gone offline despite being part of a planned shutdown. In that situation, you don't need to gather and analyze a variety of data to comprehend what transpired. Simply log the alert and carry on.

However, observability data is essential for quickly troubleshooting major issues. Although you could theoretically gather the same data that observability solutions automatically offer manually, doing so would add time to the incident response process. 

Observability tools always guarantee the availability of the data you need to understand a difficult situation. Many systems also provide suggestions or automated analyses that can speed up the process by which teams sort through extensive observability data and locate core causes of issues.

We hoped you enjoyed reading this detailed article about the key differences between Observability v/s Monitoring.