In software engineering, monitoring and observability are critical concepts that help teams keep their systems running smoothly. While they may sound similar, they have distinct differences that impact how engineers approach troubleshooting and debugging. This blog will explore the differences between monitoring and observability and why both are essential for maintaining healthy software systems.

observability vs monitoring, monitoring vs observability

Monitoring and observability are key concepts often used interchangeably when it comes to keeping complex software systems running smoothly. 

However, while both involve collecting data about the system's behavior, there is a significant difference between them. 

Monitoring tells us whether a system is up or down while observability provides a deeper understanding of what's happening inside the system, even when it's up and running. 

This blog post will explore the differences between monitoring and observability. And why software engineers must understand both concepts to ensure their systems operate as intended.

So let’s get started.

What is Observability?

Observability understands a system's internal state and behavior from its external outputs. Or, it's the ability to see inside a system and understand how it's working, even when it's complex and distributed. 

Observability goes beyond traditional monitoring by providing more context and insights into what's happening inside a system. 

By collecting and analyzing data from various sources, observability helps engineers identify and resolve issues faster and improve system performance and reliability.

How does Observability Work?

Observability works by collecting and analyzing data from various sources, such as logs, metrics, and traces, and then using that data to provide a holistic view of a system's behavior. 

This data can help engineers identify issues and troubleshoot problems faster, improving system performance and reliability.

There are three primary pillars of Observability:


Logs are records of events within a system, such as user actions, errors, or system events.

By analyzing logs, engineers can gain insights into a system's performance and identify issues affecting its operation.


Metrics are quantitative system performance measures, such as CPU usage, memory utilization, or network latency.

By monitoring these metrics, engineers can identify trends and patterns that may impact system performance. This will help them make informed decisions about optimizing the system.


Traces are records of transactions between different system components, such as a request from a user to a server.

By analyzing traces, engineers can understand how different system components interact with each other. They can also identify bottlenecks or issues that may be impacting system performance.

Also Read: Differences between K8s Cluster Autoscaler and Karpenter

Top 3 Observability Tools in DevOps


DataDog is a widely-used observability platform that provides a unified view of metrics, traces, and logs. That helps DevOps teams monitor and troubleshoot their systems.

With its powerful metrics collection and visualization capabilities, DataDog can help teams gain deep insights into the performance of their servers, applications, and databases. 

Its distributed tracing capabilities also allow teams to trace requests across multiple services. It also helps in identifying issues with service dependencies, latency, and error rates.

Additionally, DataDog's log management features enable teams to collect, analyze, and correlate logs from various sources, making pinpointing the root cause of issues easier.


Dynatrace is a powerful observability platform that offers end-to-end monitoring and analytics for cloud-native environments. 

With its AI-powered monitoring capabilities, Dynatrace can help DevOps teams detect and resolve issues before they impact end users. 

Its cloud-native support is also a major selling point, as it automatically discovers applications and services in complex, distributed systems. 

This makes it easier for teams to monitor their entire Stack, including Kubernetes, AWS, Azure, and GCP. 

Dynatrace also offers business analytics, enabling teams to correlate application performance with business outcomes. This helps teams prioritize issues that impact their business goals the most. 

Also Read: Are You Migrating from AWS to Azure?


Grafana is a highly popular open-source observability platform in the DevOps community. 

Its strength lies in its ability to visualize data in real-time, making it easier for teams to understand the performance of their systems. 

Grafana is highly flexible, allowing users to connect to various data sources, including popular cloud-native technologies like Kubernetes and Prometheus. 

Its alerting feature enables teams to set up notifications for specific events and to address issues as quickly as possible. 

Also Read: Differences between Grafana and Datadog

What is Monitoring?

While observability focuses on gaining insights into the internal workings of a system, monitoring is primarily concerned with the health and availability of a system's components.

In other words, monitoring is collecting and analyzing data to determine whether a system is functioning as expected. Monitoring tools typically rely on predefined metrics or thresholds to detect issues or anomalies. 

These metrics can include CPU usage, memory usage, disk usage, and network traffic. Monitoring tools alert system admins/DevOps teams when an issue is detected, allowing them to quickly investigate and resolve it.

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How does Monitoring Work?

Monitoring works by collecting and analyzing data from a system to determine its health and availability. 

This data is typically collected by monitoring agents or agents installed on the system being monitored. These agents collect predefined metrics or events that are then sent to a centralized monitoring tool for analysis. 

Once the data is received, the monitoring tool compares it against predefined thresholds or baselines. It is to determine whether the system is operating normally or experiencing issues. 

If an issue is detected, the monitoring tool will typically send an alert to a system administrator or DevOps team. Further allowing them to investigate and resolve the issue.

Various monitoring levels can include infrastructure monitoring, application monitoring, and user experience monitoring.

Top 3 Monitoring Tools in DevOps


Prometheus is an open-source monitoring tool that has gained significant popularity in DevOps. It is designed to monitor highly dynamic environments, such as cloud-native architectures and microservices. 

Prometheus collects time-series data using a pull model, where monitoring agents pull metrics from their monitoring systems.

Prometheus has a powerful query language called PromQL, enabling complex analysis of collected data to identify and troubleshoot issues.

It also has a large and active community, providing a wealth of plugins and integrations to extend its functionality.


PagerDuty is a popular incident management tool used to respond quickly to and resolve incidents in real time. 

The tool aggregates alerts and notifications from various monitoring tools and systems. Further, it routes them to the right team member or team for resolution. 

PagerDuty also offers on-call scheduling, escalation policies, and customizable workflows, enabling teams to tailor their incident response processes to their needs. 

Additionally, the tool provides detailed incident analytics and reporting, allowing teams to analyze incident trends and identify areas for improvement. 


Kibana is an open-source data visualization and exploration tool widely used for monitoring and analysis in DevOps environments. It is a part of the Elastic Stack, which includes Elasticsearch and Logstash. 

Kibana allows teams to visualize and explore their data in real time, making identifying patterns and troubleshooting issues easier. 

With its powerful search capabilities and intuitive dashboards, Kibana provides teams with the insights they need to make informed decisions and optimize their applications and systems.

Also Read: Top Monitoring & Testing Tools for Microservices

Observability vs. Monitoring: The Differences 

Observability and monitoring are two terms often used interchangeably in the world of DevOps, but they are not the same. 

While they are both important for ensuring the performance and reliability of systems, they have different purposes and approaches.

  • Monitoring typically involves collecting metrics and checking them against predefined thresholds. Observability focuses on understanding the behavior of a system by collecting and analyzing various data types in real time.

  • Teams often use monitoring reactively, identifying issues after they occur. While observability takes a more proactive approach, enabling teams to identify and diagnose issues before they become critical.

  • Observability involves gathering contextual data, such as logs, traces, and events, to gain a holistic view of a system's behavior. However, monitoring may only collect metrics related to specific components.

  • Observability requires sophisticated tools and data analysis techniques to make sense of the vast amounts of data collected. But monitoring tools may be more straightforward and focused on specific metrics.

  • Observability values transparency, collaboration, and continuous improvement as a culture. Whereas monitoring has a narrower scope and is seen as a more technical task.

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The Relationship Between Monitoring and Observability

In DevOps, observability and monitoring are two closely related concepts that teams frequently use together. While they have distinct differences, they are crucial for maintaining a healthy system.

Observability provides a higher level of visibility into the system. This is achieved by collecting and analyzing data from different sources, such as logs, metrics, and traces. Conversely, monitoring focuses on tracking specific metrics and thresholds to alert teams to potential issues.

Observability enables teams to detect and diagnose issues proactively, preventing them from becoming critical. Meanwhile, monitoring tracks known issues to ensure quick resolution.

Ultimately, the relationship between observability and monitoring is complementary. Observability provides a broader view of the system. 

It enables teams to identify issues more easily, while monitoring allows for targeted alerts and quicker resolution of known issues. By utilizing both approaches, teams can ensure the health and stability of their systems.

We hope that this blog on Observability vs Monitoring helps you in creating better systems for your engineering systems.

Also Read: Top 24 Best Practices for Kubernetes

Frequently Asked Questions

What is observability vs monitoring vs telemetry?

Observability is the measure of how you can understand the internal state of a system based on its external outputs. Monitoring is monitoring the system's health and performance to detect any issues. Telemetry is data collected from a system to help with observability and monitoring.

Is monitoring a subset of observability?

Yes, monitoring is a subset of observability. Observability includes putting a lot of systems in place that allows teams to spot anomalies, debug, make data-driven decisions, and more. Monitoring is one of these systems and hence, a part of observability.

Why are monitoring and observability important?

When anything out of the blue or unexpected happens, observability helps you get back in control and know exactly what is happening with your software.

Monitoring, on the other hand, is important because it helps you stop when something expected happens but in an unexpected way. This helps you prevent downtime of your software.