Observability vs. monitoring: What's the difference?

What is monitoring?

Monitoring is the process of collecting and analyzing metrics related to the health and performance of IT infrastructure components, applications or services. By doing so, organizations can ensure that IT resources are operating normally. There are two main reasons why organizations use monitoring.

The first reason is trend analysis. Trend analysis uses monitoring data collected over time to spot long-term trends. In the case of an application, this might mean tracking how its performance changes over time. By analyzing various trends, it sometimes becomes possible to predict future behavior. As an example, an organization might use trend analysis to track how well an application scales as demand increases. By analyzing such data, an organization might be able to predict the point at which increased demand would affect the application's performance or stability.

Trend analysis is also useful for infrastructure capacity planning. For example, organizations typically track storage consumption to predict when they will need to invest in additional storage to avoid running out of space. Similarly, monitoring CPU or memory usage might signal the need for a hardware upgrade before performance begins to suffer.

Monitoring is also used for event detection. Monitoring systems are often paired with alerting mechanisms that draw an administrator's attention to errors, security incidents or other conditions that might need to be addressed. It's unrealistic to expect an administrator to spot every potentially problematic condition in real time, so automated monitoring and alerting are essential for keeping applications and infrastructure healthy. This type of monitoring is essential to maintaining a good UX.

Common monitoring metrics

Every monitoring tool has its own approach, but the following list encompasses some of the more frequently used monitoring metrics:

• CPU utilization.
• Memory utilization.
• Disk I/O.
• Disk space utilization.
• Server uptime.
• System load average.
• Application response time.
• Application error rate.
• Application throughput.
• Application availability.
• Database query rate.
• API response time.
• Network bandwidth utilization.
• Network latency.
• Network packet loss.
• Network errors.
• Network jitter.

Monitoring tools

The following are some of the more popular monitoring tools:

• ManageEngine Applications Manager. Good for organizations that want to ensure their applications are healthy and performing well.
• Catchpoint. Ideally suited to large enterprises that have an e-commerce presence or remote employees all over the world.
• Grafana Cloud. Well-suited for cloud native environments in which traces must be performed across distributed systems
• Kentik. Good for medium- to large-sized enterprises that need deep visibility across large, extremely complex distributed hybrid networks.

What is observability?

Observability is a technique often used to assess the health and performance of IT workloads. Unlike monitoring, which focuses on detecting problems, observability seeks to understand why problems occur. It accomplishes this by aggregating data from a variety of available sources -- such as logs, metrics and traces -- and then using that data to derive information about the system's overall health and performance with the goal of providing a better overall UX.

Observability tools are rooted in control theory, which roughly states that a system can be understood by examining its inputs and outputs. As such, observability isn't about collecting every conceivable piece of data; rather, it's about strategically examining the data that enables the software to make meaningful and accurate assessments.

Although the resulting assessments can be high-level, observability's real strength lies in its granularity. Observability software might initially display a high-level view of a system, but it lets the technician drill down into the individual components that make up a distributed system or application. In fact, observability techniques are often used by root cause analysis tools.

Observability matters because it helps IT pros understand why things are going wrong, not just that something has broken. This, in turn, allows for faster troubleshooting when problems do occur, but observability tools often proactively identify issues and assist with resolving them before a problem actually occurs.

The 3 pillars of observability

Observability is often described as consisting of three pillars: metrics, logs and traces.

Metrics

Essentially, metrics are measurements of a particular resource, such as those metrics gained through performance monitoring. For example, database metrics might be based on the number of transactions occurring each second. Similarly, OS metrics might examine the percentage of CPU resources in use or the amount of memory that's currently being used. Metrics give IT pros a way to know what values are normal for a particular system so abnormal conditions can be more easily recognized.

Logs

Simply put, logs are automatically generated records of various types of events. Log contents vary by system and by log type. Some logs are general in scope, while others focus on a specific topic, such as security or a particular service or application. Logs generally contain errors, warnings and relevant events. These events might include user logons, a service starting up or a particular resource being accessed.

Traces

Sometimes called distributed traces, traces are designed to track how application or infrastructure components work together. An application trace, for example, might track how various application components are used during a particular task. Similarly, a network trace tracks packets as they flow across a network. 

Observability tools

The following are some of the more popular observability tools:

• Amazon CloudWatch. The best option for observing resources in the AWS cloud.
• Datadog. Offers over 900 integrations, making it a good choice for organizations that depend heavily on third-party software.
• Dynatrace. Particularly well suited for large language models and AI agents but can monitor a wide range of third-party technologies.
• Grafana. A great choice for those who like rich dashboards and insightful visualizations.
• IBM Instana Observability. A good tool for those who prefer simplicity, thanks to its single-agent architecture and automatic discovery capabilities.
• New Relic. A good option for organizations that want full-stack monitoring -- both application and infrastructure -- paired with root cause analytics.
• ServiceNow. A good choice for those who need real time visibility into their applications and infrastructure.
• Splunk AppDynamics. A great tool for performing root cause analytics, but ideal for those who need to detect problems at the code level.
• Sumo Logic. A good option for those who need to combine observability with compliance monitoring.

How are monitoring and observability related?

There are similarities between monitoring and observability. For instance, both monitoring and observability aim to provide IT professionals with better insight into the health of the systems they oversee. Monitoring and observability are also sometimes based on the same sources of information. This can be especially true for logs and metrics.

 Observability vs. monitoring: Key differences

In some ways, observability could be thought of as an extension of monitoring. After all, both monitoring and observability use available information to help admins better understand what's going on with their systems. However, monitoring tends to be a bit broader in scope, whereas observability is more focused on a system's current state of health and functionality. In doing so, observability solves a key problem.

Monitoring is great for detecting problematic conditions and spotting long-term trends, but it isn't the best tool for troubleshooting complex systems. Although the root cause of the problem might be revealed in the monitored logs, sifting through them can be tedious and time-consuming, and the people reviewing the data must have some idea of what they're looking for. When observability is used, it becomes much easier to pinpoint the component causing the problem. To put it another way, monitoring is reactive, showing you problems that have already occurred. Conversely, observability enables you to proactively address issues before they become problems.

Another key difference is that monitoring often relies on static dashboards that provide a fixed view of metrics. Although this information is useful, monitoring systems can miss key details of issues that might be occurring subtly. On the other hand, observability tools encourage dynamic exploration. This flexibility is essential for quickly resolving problems with complex systems.

One more difference is that monitoring tends to focus on known issues, such as a server being down or a web application slowing to a crawl. Although observability tools can be used to troubleshoot known issues, they are also useful for detecting previously undiscovered issues.

Choosing between monitoring and observability

Although it's only natural to wonder which is best, remember that monitoring and observability serve two different purposes. Monitoring tends to be best suited for long-term trend analysis and alerting to potentially problematic conditions. Conversely, observability might provide greater insight into system health and help an organization be more proactive in addressing issues before they become problems.

The key takeaway is that monitoring and observability aren't mutually exclusive. There is no rule requiring an organization to use one or the other. In fact, an organization that wants optimal insight into its IT systems might use both. Likewise, an organization might find that monitoring is a better option for some workloads, while observability is the better choice for others.

Conclusion

Both monitoring and observability are crucial for maintaining healthy and reliable systems. Monitoring provides real-time alerts and historical trends, while observability helps IT pros to peer deep into complex systems to resolve issues. 

Brien Posey is a former 22-time Microsoft MVP and a commercial astronaut candidate. In his more than 30 years in IT, he has served as a lead network engineer for the U.S. Department of Defense and a network administrator for some of the largest insurance companies in America.