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What is the difference between observability vs. monitoring?

Observability means understanding the state of a system based on its outputs, while monitoring is tracking metrics to assess the health of a system.

For as long as business and information technology have been intertwined, companies of all shapes and sizes have been seeking ever-more accurate means by which to understand, assess, and optimize the computer systems they rely on. As early as the late 1980s, interactive, real-time monitoring tools were helping organizations track IT health and performance. But as digital technology has evolved, business systems have likewise become much more robust, distributed, and complex, presenting a new set of challenges for organizations.

Specifically: How can a business take into account and aggregate the potentially billions of data points across dynamic, distributed environments to create an accurate picture of what’s happening in their digital systems?

Traditionally, monitoring has been the go-to method for IT management, providing valuable insights into the health and performance of various components within a system. But with the rapid evolution of technology, monitoring alone falls short in providing the comprehensive understanding needed to navigate the complexities of today's IT environments. This is where observability comes into play.

To address the challenges and intricacies of managing a modern IT landscape, monitoring and observability are essential methodologies. While both serve the same purpose of providing insights into complex systems, they offer distinct approaches and benefits that set them apart.
Graphic lining out how observability and monitoring work.


Monitoring, in the context of IT management and DevOps, refers to the process of systematically tracking and collecting data from various components within a system. Through predefined metrics and data points, monitoring aims to provide real-time visibility into the health, performance, and availability of critical resources. This practice allows IT teams to identify potential issues, anomalies, and performance bottlenecks, enabling them to take proactive measures to maintain system stability and minimize downtime.

The key benefits of monitoring are:

  • Alerting and incident response
    Monitoring systems are equipped with alerting mechanisms that notify IT teams when specific thresholds or predefined conditions are breached, indicating potential problems that may require immediate attention.
  • Historical analysis
    Monitoring generates valuable historical data, which can be used to track trends, conduct post-incident analyses, and make informed decisions for capacity planning and future improvements.

What is observability?

Observability, on the other hand, represents a more comprehensive and holistic approach to understanding complex systems. While monitoring provides specific data points and metrics, observability focuses on the ability to gain insights into the system's internal state based on external outputs. It seeks to answer the question “Why is the system behaving this way?” Observability provides a more holistic look at the system so that IT behavior may be understood as a whole rather than just isolated parts.

As such, the benefits observability provides are different from those associated with monitoring. These benefits include:

  • Context-rich insights
    Observability emphasizes context-rich, fully correlated information, offering a deeper understanding of the relationships and interactions between various system components. This makes it easier to discover the root causes of potential issues, rather than just being notified that the issues exist.
  • Unforeseen discoveries
    Unlike monitoring which operates on predefined metrics, observability allows for the discovery of previously unknown issues or patterns, empowering teams to explore beyond what they are already familiar with.
  • Adaptability to change
    In rapidly evolving environments like microservices and container-based systems, observability allows organizations to adapt more readily to changes in the system's architecture and infrastructure—an essential element when working with complex and distributed environments.
  • Performance optimization
    By analyzing the collected metrics, IT professionals can identify areas of inefficiency or underutilization, allowing for targeted performance optimizations and resource allocation.

The difference

Both observability and monitoring share a nearly identical mandate: to help organizations understand and manage their complex IT systems by identifying problems. But while these two approaches may be different sides of the same coin, their differences are what define them. Each takes a specific route to accomplish its goal and provides unique insights that DevOps and IT managers can use to improve application and system transparency.

The most important differences between observability vs. monitoring are:

  • Data collection approach
    Monitoring typically focuses on predefined metrics and specific data points, while observability embraces a more open-ended approach, allowing for the collection of various data streams, logs, traces, and events. Observable systems produce relevant data about their internal states that may be used to infer the current status of the system, as well as discover root causes of possible problems.
  • Infrastructure
    Traditional infrastructure solutions, such as data centers, often rely on monitoring to track performance, while cloud environments and other modern infrastructures lean more towards observability. Observability can and should be applied across both traditional and modern architectures, but with the understanding that monitoring often struggles to cope with the data volume generated by modern infrastructures, often leading to blind spots in visibility.
  • Causality vs. correlation
    Monitoring provides correlations between events and metrics, while observability seeks to establish causality. Simply put, monitoring involves keeping a close eye on metrics and thus is used to inform DevOps teams when something is not functioning properly or when other problems arise, but it’s not as effective at identifying the actual issue at the heart of the problem. Observability enables IT teams to understand the "why" behind a system's behavior.
  • Predictability vs. adaptability
    Monitoring's predefined thresholds allow for predictability, whereas observability's dynamic nature makes it better suited for adapting to unforeseen changes and complexities.
  • Granularity
    Monitoring data collection typically requires an agent installed on the host, capturing metrics from an outside-in perspective. In contrast, observability is generally instrumented at the code-level, providing a level of granularity that monitoring struggles to achieve. This allows for deep insights into the behavior of individual components and services.
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Monitoring has always played a crucial role in providing real-time insights into the health and performance of computer systems, and continues to do so today. Through a systematic data collection and analysis process, monitoring allows IT professionals to detect potential issues and inform their strategies for maintaining optimal system operations.

This process includes the following steps:

1. Data is collected for analysis

Monitoring entails the collection of data from a system, capturing crucial metrics and events that reflect its overall state. To gather this data, monitoring agents are installed on the system. These tools are responsible for collecting predefined metrics or events that serve as indicators of system health and performance.

2. Data is sent to a central monitoring tool

The data collected by monitoring agents is transmitted to a centralized monitoring tool or platform, where it undergoes further analysis. Within the monitoring tool, the collected data is compared against predefined thresholds or baselines. These thresholds serve as benchmarks for what is considered normal behavior within the system.

3. Issues are detected and alerts sent

When the monitored metrics or events deviate from the established thresholds, the monitoring tool identifies these potential issues or anomalies. In response to detecting an issue, the monitoring tool triggers alert notifications, typically sent to system administrators or DevOps teams. These alerts serve as early warnings, prompting further investigation and swift resolution of the problem.

Monitoring can be applied at various levels, each with a specific focus:

  • Infrastructure monitoring
    This level assesses the health and performance of hardware resources, network components, and virtual machines.
  • Application monitoring
    Application monitoring This is focused on monitoring the behavior and performance of software applications and services.
  • User experience monitoring
    This level provides insights into the end-user experience, measuring response times and user interactions.

Observability works by collecting and analyzing data from diverse sources to paint a comprehensive and dynamic picture of a system's behavior. This holistic understanding empowers engineers to identify the root causes of issues, troubleshoot problems faster, and enhance system performance and reliability—even in complex, distributed systems and applications.

Observability platforms may employ different processes for collecting and analyzing system outputs, with some of the most innovative approaches employing AIOps and machine learning capabilities to distinguish relevant data from less-important information. But regardless of the methodologies in play, most approaches to observability focuses on three essential pillars:


Logs—the oldest form of telemetry—are detailed records of events within a system, capturing information about user actions, errors, warnings, and system events. These textual representations provide a valuable historical trail, allowing engineers to understand the sequence of actions and occurrences that take place within the system over time. Despite their origins in the early days of the digital era, logs are still considered crucial elements in observability investigation.


Metrics involve the collection of quantitative measurements related to system performance. These measurements may include CPU utilization, memory usage, network traffic, response times, and more. By monitoring metrics, IT professionals gain the benefits of real-time insights into how the system is behaving and performing, and where potential problems may be coming from.


Traces capture the interactions between different components of a system as transactions flow through them. These interactions may include user requests, API calls, or service invocations. Traces help engineers visualize the path of a transaction across the system, uncovering potential bottlenecks and areas of improvement.

The synergy between logs, metrics, and traces forms the bedrock of observability. Each of these components contributes unique insights into a system's behavior, providing IT professionals with a comprehensive view of their applications and infrastructure.

While observability offers a powerful approach to gaining a comprehensive understanding of complex systems, it is not without its challenges. Overcoming these obstacles is crucial for seeing into the causes at the root of system behavior and harnessing the full potential of system and application insights.

Some of the more prominent challenges include:

Infrastructure complexity

Observability was developed to provide clearer insights into the dynamic landscape of multi-cloud environments and microservices, but that does not mean it’s always a painless process. Implementing observability into complex systems can be challenging; the intricate nature of distributed systems and containers demands specialized solutions for aggregating data across various platforms. Achieving observability into these complex infrastructures requires careful planning and tailored solutions to capture a holistic view of the system's behavior.

Data volume

Observability generates vast amounts of data, potentially overwhelming DevOps and other IT teams who are responsible for turning observability strategies into actionable insights. Without the right platform and support tools, this flow of data can quickly get out of hand.

Data silos

To achieve effective observability, collaboration across DevOps, engineering, and business teams is essential. When teams operate in isolation, it can lead to tool sprawl and result in higher engineering hours and costs. Unfortunately, existing data and infrastructure silos can hinder the implementation of a unified observability solution. Breaking down these silos and fostering cooperation is key to leveraging observability's full potential.

With the growing complexity of modern infrastructures, finding ideal tools that cater to the specific needs of the organization is more important than ever. Here are key factors to consider when evaluating observability and monitoring tools:

  • Data collection and integration
    Effective data collection is foundational for both observability and monitoring. Consider only those tools or frameworks that can gather data from a broad range of sources (logs, metrics, traces, and more). These solutions should also be capable of integrating with various types of platforms—on-premises, cloud-based, and hybrid environments—to create a real-time unified view of the organization's IT systems.
  • Analytics and visualization
    Advanced analytics capabilities in observability and monitoring solutions enable teams to uncover trends, patterns, and anomalies that might impact system performance and reliability. The most effective toolsets also provide intuitive and interactive data visualization, allowing IT teams to interpret complex information at a glance.
  • Scalability and consistent performance
    In the face of evolving infrastructures and increasing data volumes, observability and monitoring tools must be easily scalable, capable of handling the organization's growth. High-performing solutions should accommodate the demands of dynamic systems and provide real-time insights without sacrificing accuracy.
  • Strong community and support
    For open source solutions, active communities often offer valuable insights, resources, and support to users, enhancing the tool's overall utility and allowing for more effective troubleshooting and user self-service. A strong community and reliable support are essential to geting the most out of these open-source solutions. Explore the community and user base around the tools under consideration.
  • Ease of use and training resources
    User-friendly interfaces and comprehensive training resources can significantly impact the tool's effectiveness. Seek tools that are accessible to users of varying technical backgrounds and offer educational materials to help promote proficiency.
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While monitoring provides real-time visibility into system health and performance through predefined metrics, the right approach to observability takes a more holistic approach, offering context-rich insights into the internal state of systems based on external outputs while also handling the core monitoring workflows that organizations and enterprises depend on. Together, these methodologies equip IT teams with a comprehensive toolkit to navigate the challenges of modern business operations. Unfortunately, achieving observability in complex distributed systems presents its own set of challenges. To address these challenges, ServiceNow introduces Cloud Observability.

Cloud Observability brings together critical telemetry data into a single solution, facilitating seamless integration into essential business operations. Easily identify issues, proactively avoid outages, and protect revenue while boosting customer satisfaction. Work within intuitive dashboards, take advantage of a unified query language, and apply a correlation engine for reducing mean time to resolution (MTTR)—all on a centralized platform, for a comprehensive single source of truth for your essential system data.

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