Observability vs Monitoring: Key Differences Explained

Observability vs Monitoring: Key Differences Explained

Monitoring has always been a crucial operation in a software development cycle. This is mainly because of the complexity of industry-level IT and consumer-facing product development. Additionally, there is an ever-growing demand for rapid upgrades in products. To meet these requirements, streamlined performance and stability have become more important than ever; and without effective monitoring practices, they appear difficult to achieve.

However, recent times have seen a gradual shift from the term ‘monitoring’ to ‘observability’. Some mark this as a DevOps-ification of monitoring, while others opine that it is much more than just a refurbished version of monitoring. To put it simply, monitoring is for notifying you when something goes wrong in your application, and observability is to help you understand why that is. Read along, as we dive deeper to get a better understanding of the two and help you identify the right one for your use case!

Here’s what we’ll cover:

What is Monitoring?

A monitoring system provides key insights and information about an application’s performance and usage patterns. This includes information about memory issues, bottlenecks, request rates, availability, server health, end-user experience, and so much more.

Goals of Monitoring

There are multiple goals defined for a monitoring system.

High Availability of Application

The first and foremost aim of a monitoring setup is to ensure maximum availability and uptime – that the application is available for as long as possible. While a monitoring system (or any system for that matter) can not fix bugs that arise during uptime, it sure can report them as quickly as possible and help the DevOps team to act on them. 

Here, Time to Detect (TTD) and Time to Mitigate (TTM) are two important metrics to measure the reliability of an application. TTD is aimed at determining how fast the issues are reported to the concerned team in an application. TTM defines how fast the team can respond to the issue and fix it back to normal. While the TTM metric depends upon the issue as well as the capacity of the DevOps team, a great monitoring setup can ensure good TTD values, thereby alleviating a great amount of delay in the process.

Validated Learning by Analyzing Usage

Application usage statistics are extremely valuable to product teams. While a user may write a descriptive review of your application on demand, their usage statistics might provide a better insight into how well they have been able to utilize the application. Similarly, monitoring solutions can also be made smart enough to validate the changes in performance across multiple deployments by analyzing the change in usage statistics. If an update marks a lower usage trend in the feature addressed by the update, it can be considered as a clear bad sign, and the product team can then work to improve it. If the usage trends indicate a surge in acceptance of the update, the team can gear up to deliver similar updates faster. In this way, monitoring setups can serve as a great tool for driving product decisions.

How Does Monitoring Work?

Monitoring is carried out using a special set of processes called Telemetry.

Telemetry refers to the automated collection of your application’s real-world usage data. It provides the raw data about usage patterns from the user, which is then converted into actionable insights. This is made possible using server logging, SDKs installed in source code, agents installed in the deployment environments, or anything similar.

There are many types of monitoring. Some of the top ones include:

Synthetic Monitoring

Synthetic Monitoring is a process of monitoring an application by simulating the effect of real users. It helps drive business decisions as target simulations can be easily carried out to test out the performance of specific features intensely.

It works by issuing automated in-app transactions via scripts or bots and monitoring the outcome of the activity. It helps in testing out the application’s performance even with low (real) user activity and identifying issues beforehand. This can be carried out either by simulating a browser or driving an actual browser via scripts.

Real User Monitoring

Real User Monitoring, on the other hand, utilizes real users’ data to measure user experiences. Instead of simulating a user, it derives insights from actual user data compiled from application usage.

Real User Monitoring (RUM) puts the user first and provides insights based on human interaction – a factor that bots almost always fail to emulate. The only quirk here is that RUM requires ample user traffic to be able to provide useful insights.

As is made obvious from this section, monitoring often serves as a “test in production”. A well-monitored system constantly transmits your application’s health data, which allows DevOps teams to spot and fix errors swiftly. Now let’s look at what Observability entails.

What is Observability?

Observability, a concept which originates from the realms of control theory, is defined as the ability of a system to enable identifying its internal states by analyzing its external outputs. It leverages instrumentation to provide insights that enhance monitoring.

If a system is observable, it means it is easy to understand and gauge its internals and navigate to the root cause of issues faster. Observability is more of a system’s ability to diagnose what’s happening inside, and less of a tangible tool to measure a system’s performance.

Pillars of Observability

Observability can be represented by its 3 primary pillars:


Logs are records of events that happen in a system. These are automatically generated, timestamped, and stored immutably. They provide a complete and targeted record of distinct events, along with additional metadata about the system state during the time of the event. Although logs may be stored in plain text files, it is advisable to store them in a specific set format like JSON, so that they may be indexed easily by log visualization tools.


Metrics are a numerical representation of data measured over set periods. They are the foundations of monitoring, and frequently carry information about how much time or memory is being taken by specific operations. However, they are not similar to event logs, which record specific events. They target the entire system’s performance to draw out useful conclusions.


Traces are well-documented records of a series of related events happening in a network. They focus on providing request-wise data, and they can track requests through multiple applications as well. Each trace focuses on one request as it travels through multiple points before finally being responded to, and traces are extremely useful in determining the faulty part of the system as a whole. They resemble end-to-end testing in software development and are used to gain a high-level insight into the health of an application.

Goals of Observability

Using these pillars, an observable system strives to achieve the following goals–


One of the most important goals for both monitoring as well as observability is reliability. The reliability of a software system refers to the probability of its error-free or failure-free operation for a specific period of time (i.e. its ability to function without failure). Observability exposes issues in the system that can be immediately targeted and debugged before they fail your application.

Business Growth

As observability involves handling actual user usage data, this data can be put to great use. Businesses can drive their revenue growth strategies based on how well their users are accepting their updates and new features. The ability to analyze analytics data on the fly opens up a whole new world of possibilities of user-driven product development. This data can be used to build useful insights on how to optimize the product and generate more revenue from customers.


The observability of cloud-based applications is extremely important in maintaining their security standards. Unintended exposure of application data can be easily tracked and fixed. With adequate visibility into the application and its environment, security teams can detect potential intrusions, threats, and attempted attacks before they are even completed.

The difference between Observability and Monitoring

Having understood both Observability and Monitoring, we can now move forward to understanding the differences between the two. While they seem to be highly similar to each other, quite many differences exist between them. Some of them include–

Monitoring is a Process, while Observability is a Property

As we’ve seen in the previous sections, monitoring is a distinct set of ways in which an application’s performance can be measured and tracked, while observability is a system’s ability to be transparent enough to facilitate easy tracking of issues. While observability determines the standards that should be met for a system to be monitored properly, monitoring acts on those norms and does the job.

Observability is a Superset of Monitoring, and it Encompasses Many Other Practices

There have been many mentions of monitoring being a part of the observability process, along with other aspects like alerting, tracing individual requests, and aggregating logs. As mentioned in the three pillars of observability, it thrives on logs, metrics, and traces. 

You can log tons and tons of data from your application, but if they do not help you solve an issue quickly, your system will be known to possess very low observability. On the other hand, if you can solve issues frequently with limited logs and traces, your system will be known to have great observability. The fundamental goal of observability is to be able to provide enough data from the system to be able to solve present and potential issues. Monitoring aims only to collect data and notify the team in case of errors.

Monitoring is Aimed At Reporting Known Errors, While Observability Tries to Find Issues That Haven’t Been Discovered by Users Yet

One of the most important differences between observability and monitoring is that monitoring is always on the lookout for issues that are known to the user. While Synthetic monitoring does take a shot at testing the unknown, observability’s foundational principle is to be able to handle present errors as well as identify incoming issues before they are discovered by the users. 

Advantages of Having Observability

There are multiple advantages of having proper observability set up for your system over having just the barebones monitoring. Some of them are–

Advantages of Monitoring Performance

Having understood the benefits that observability brings to the table, let’s now analyze the advantages offered by monitoring solutions to the management of an application–

H2: Observability and Monitoring in Development Lifecycles

The software development life cycle (SDLC) highly values the monitoring phase once the software is deployed. This is usually so because most of the modern SDLC models include a feedback process in which the deployed application is tested and then changes are made to introduce improvements. Monitoring plays a crucial role here as it enhances the process by collecting extensive usage patterns and statistics. Let’s take a look at the various ways in which the use of monitoring and observability improves the software development process.

The Need for Observability in DevOps

If you go back to the times when the waterfall model of SDLC was popular and Agile was taking its baby steps, you can recall the hassles associated with testing the deployed application. This used to take monitoring and infrastructure operations out of scope, as no process could utilize them effectively.

The approach used earlier was to build things for success, and often in the process, developers used to fail to make their applications reliable. This used to push out a lot of less dependable apps. Information from monitoring setups used to be insufficient, and a lot of metrics in applications used to remain unknown, and thus unutilized.

The growing issues due to improper monitoring led to the widespread adoption of observability practices and changed the SDLC process for good. Monitoring goals are no longer limited to merely collecting and processing data, metrics, and traces; they have shifted to making the system more observable. Modern monitoring, aided by effective observability practices, has become smart enough to be able to predict and fix issues before they can be discovered by the users.

How Monitoring Helps Businesses Save Money

Monitoring is an extremely powerful tool, and there are multiple ways in which it can help businesses save money. Here are some of them–

Avoiding downtime

One of the biggest reasons why products lose users is unexpected downtimes. Monitoring solutions reduce this by detecting end-user experience issues early, much before they cause downtime. They can be utilized to automatically and proactively identify first occurrences of issues that may have the potential to disrupt the entire system.

Pinpointing Issue Ownership Quickly

When downtime occurs, the biggest priority of the on-call team is to identify who needs to work on it. IT infrastructures are large and distributed, and finding out whose infrastructure component is at fault can be a time-consuming process. However, this is important as the right person can speed up the process and fix the system quite easily, while others might struggle for hours at it. Monitoring solutions can help in tracking down the component that went off very quickly and directing the issue to its rightful owner.

Lowering MTTR

In case of errors, the mean-time-to-repair (MTTR) holds a lot of significance, and the perfect monitoring solution for a project can bring the MTTR down from hours to minutes. When a fault occurs, the monitoring solution can quickly identify the domain at fault, which can indicate what resources need to be allocated to solve the issue.

Accelerating Release Cycles

Modern software development processes depend upon user feedback to improve. A great monitoring solution can help get the right insight into what’s going down well with the users, and motivate the product team to deliver better and on-time. A faulty monitoring solution, on the other hand, can delay this process, and can even negatively impact the growth by sending in irrelevant/incorrect statistics.

Additionally, monitoring solutions can also be leveraged to monitor the performance of applications while they are under development, and prevent unnecessary issues from seeping into the production environment.

Calculating Capacity Better

Apart from helping with issues on the user’s end, monitoring solutions can also help you cut down on unnecessary capacity costs. In most cases, unusually high consumption rates signify a performance issue or a resource leak. Monitoring solutions can help identify these early, and stop them before they burn a hole in the pocket.

How to Get Started with Observability and Monitoring

Owing to the sophisticated process that observability has grown to become, it requires an equally effective set of tools and approaches to monitor, analyze and trace events. A simple outline that can be followed to best implement observability is described below–

Choose a Centralized Monitoring Solution and Get Started

A great observability set-up always begins with a great monitoring foundation. The first step in the process is to look for the monitoring solution that best suits your needs. An important point to focus upon in this step is to make sure that your monitoring platform unifies data from all of your software platforms into one interface. This is crucial as distributed data adds an overhead of unifying the data manually before moving on to analyzing it. 

Another important factor to consider when choosing a monitoring solution is to take into account the growth that your requirements can undergo in the coming times. This is one of the most common mistakes made by developers and project managers alike – the right platform today isn’t necessarily the right platform months or years from now. If you talk about technologies that are comparatively new, like microservices and Kubernetes, you are probably going to see drastic changes in usage as well as other trends in the coming times. It is important to go for something that suits your requirements right now and is flexible enough to accommodate your changing requirements in the future.

Analyze the Metrics of Your Application

There are many things to take care of in this step. First, it is important to carefully analyze your metrics, and make sure to take into account all metrics of all your applications, which is why we were focusing on having a centralized solution in the last step. Any missed metrics might present an inaccurate trend, and fetching or combining metrics from multiple platforms will make the troubleshooting process unnecessarily cumbersome.

Next, it is important to choose your metrics carefully. Each monitoring solution can present you with a huge number of performance metrics and usage statistic alternatives, but not each one of them will be useful for you. Apart from having redundant data, it will also make navigating across the data for finding the right pieces difficult. The real aim of observability is to provide you with data that’s useful to you, no matter how many metrics are required to do so.

Finally, having a good log analysis setup is crucial too. Even the most general metrics are bound to generate tons of data over long periods. The efficiency of a monitoring setup depends on how quickly you can get to the logs that captured information about anomalies/errors. Coupling log analysis with monitoring is a great alternative to consider, as it takes off the burden of manually setting up a tool for it.

Respond to the Trends in the Statistics

Finally, when you have set up the centralized monitoring platform and have also received some useful logs to work with, the only question left is – what should you do with the data? 

One good way of approaching this is to introduce machine learning in the process – throw in some successful algorithms that can help to automate the process of exponentially accumulating the data collected and turn them into useful insights that can aid you to do more in less time. With a setup like this, you can set up dynamic thresholds, identify anomalies and find the root causes of these issues. Over time, machine learning and algorithmic systems can identify issues proactively by analyzing what’s not normal for your system, and raising an alert before the impending issue drives your application off the cliff.

Ultimately, it lies in the hands of the DevOps team to receive alerts as soon as they are created and respond to them at the earliest. A strong and capable team will beat any issue at the end of the day.

Over to You

Observability and monitoring work best when they complement each other. While it is important to understand how different they are from each other, it is more important to know which one has to be used in your application. If you have an application that is huge and handles sensitive data, perhaps a full-fledged observability solution should be your go-to. However, if your application is of a relatively smaller scale and you are sure that you won’t be constantly targeted with illicit intrusions, you can save a few dimes by going for a monitoring-only alternative. No matter which one you go with, it is important to fulfill the two goals - reliability and security.

Looking back at the post, we talked about observability and monitoring in great detail. Starting with independent discussions over the two, we moved on to compare and contrast the two practices. Once we had a clear picture of the two topics, we discussed each of their advantages and importance in software development lifecycles industry-wide

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