Observability vs. monitoring: What’s the difference?

Imagine a video streaming service decides to live stream a midnight concert featuring a popular music artist, but when users log on at midnight to watch it, they experience buffering issues. Some of the artist’s dedicated fans might stick around to see if the issue improves. However, casual fans might abandon the stream; and worse yet, frustrated superfans might abandon the stream and the streaming service.

Today’s tech consumers expect lightning-fast speeds, ultrahigh uptimes and seamless interactions. Negative user experiences—such as buffering problems during a big concert—can increase customer churn, so IT teams need the ability to quickly identify root causes and resolve system issues.

This is where monitoring and observability tools become indispensable to modern IT operations (ITOps). Let’s look at how such tools could not just solve, but prevent, such a scenario.

To address buffering issues on a live stream, an operations team can use a monitoring tool to notify them when a group of servers has exceeded load thresholds. The team can then rebalance server load by redistributing traffic across available servers.

Triggered by the monitoring alert, an observability platform can analyze key metrics (such as bit rate adaptation) and use distributed traces to follow video requests and identify where the buffering begins. If, for instance, the tool finds that the buffering issues stems from underperforming content delivery network (CDN) nodes, it can provide IT personnel with options for optimizing CDN configurations and improving device compatibility.

In fact, leading observability tools can analyze historical monitoring data for similar network events and predict that the concert will overload CDN nodes in a particular region. The tool can prompt IT staff to proactively reconfigure the CDN, addressing the slower nodes before they create buffering problems for users.

In short, monitoring and observability offer businesses complementary approaches to diagnosing system issues. Whereas monitoring tells teams when something is wrong, observability tells them what’s happening, why it’s happening and how to fix it. Used together, they enable the comprehensive issue detection and resolution capabilities IT teams need to ensure seamless customer experiences.

To better understand the difference between observability and monitoring, let’s look at how each works, their similarities and differences, and the roles they play in software development and network management.

Observability is the ability to understand a complex system’s internal state based on external outputs. When a system is observable, IT teams can identify the root cause of a performance problem by looking at the data it produces. There is no need for extra testing or coding.

The term “observability” comes from control theory, an engineering theory concerned with automating control of dynamic systems (regulating the flow of water through a pipe based on feedback from a flow control system, for instance). Modern vehicles serve as another example. Car diagnostic systems often provide observability for mechanics, who use them to figure out why a car won’t start without having to take it apart.

In ITOps and cloud computing, observability requires software tools that aggregate and correlate steady streams of performance data from applications and hardware and networks they run on.

Observability solutions (such as OpenTelemetry) can analyze a system’s output data, provide an assessment of the system’s health and offer actionable insights for addressing problems. Teams can then use the data to monitor, troubleshoot and debug apps and networks.

An observable system is one where DevOps teams can see the entire IT environment, including contextual data and interdependencies. The result? An IT architecture that enables teams to detect problems proactively, resolve issues faster, optimize the customer experience and meet service level agreements (SLAs).

Monitoring assesses system health by collecting and analyzing aggregated data from IT systems, based on a predefined set of metrics and logs. In DevOps, monitoring measures application health to detect known failures and prevent downtime. An IT team might, for instance, create a rule within a monitoring tool that alerts team members when an app is nearing 100% disk usage.

Where monitoring truly shows its value is in analyzing long-term trends. A monitoring tool can show teams both how an app is functioning and how it’s being used over time. However, monitoring has its limitations.

For monitoring to be effective, teams must know which metrics and logs to track. If the team hasn’t predicted a problem, monitoring tools can miss key production failures and other issues. Monitoring also requires IT staff to manually correlate data across siloed monitoring tools, making root cause analysis a more complex and time-consuming process and limiting developers’ predictive capabilities. 

The terms “observability” and “application performance monitoring” are often used interchangeably. However, it’s more accurate to view observability as an evolution of application performance monitoring.

Application performance monitoring refers to the tools and processes that help IT teams determine whether applications are meeting performance standards and user expectations. Monitoring tools typically track network infrastructure health and performance, application dependencies, business transactions and user experiences. These systems aim to quickly identify, isolate and solve performance problems.

APM was the standard practice for more than two decades, but with the increased use of agile development, DevOps, microservices, multiple programming languages, serverless and other cloud-native technologies, teams needed a faster, more comprehensive way to monitor and assess highly complex environments. APM tools designed for a previous generation of application infrastructure could no longer provide fast, automated, contextualized visibility into the health and availability of an entire application environment. New software is deployed so quickly today, in so many small components, that traditional APM tools have trouble keeping up.

Enter observability. Observability builds upon data collection methods from application performance monitoring tools to better address the distributed, dynamic nature of cloud-native application and service deployments. Observability solutions take a holistic approach to logging and monitoring, helping teams better understand how services interact (with dependency maps, for instance) and fit into the overall architecture.

The difference between monitoring and observability is often the difference between identifying problems that you know will happen and finding ways to anticipate problems that might happen. At their most basic, monitoring is reactive, and observability is proactive. However, both use the same type of telemetry data, known as the three pillars of observability.

The three pillars are:

• Logs: Records of what’s happening within your network and software systems. Logs provide granular information about what occurred, when it occurred and where in the network it occurred.

• Metrics: Numerical assessments of system performance and resource usage. Metrics provide a high-level overview of system health by capturing specific data types and key performance indicators (KPIs), such as latency, packet loss, bandwidth availability and device CPU usage.

• Traces: End-to-end records of every user request’s journey through the network. Traces provide insights into the path and behavior of data packets as they traverse multiple devices and systems, making them essential for understanding distributed systems.

In monitoring, teams use this telemetry data to define thresholds and benchmarks, and create preconfigured dashboards and notifications. They can also use telemetry to identify and document dependencies, which reveal how each app component works with other components, applications and IT resources.

An observability platform takes monitoring a step further. Observability platforms also use telemetry, but they use it in a proactive way.

DevOps, site reliability engineers (SREs), operations teams and IT staff use observability tools to correlate telemetry in real time and get a complete, contextualized view of system health. This enables teams to better understand each element of the system and how different elements relate to each other.

By providing a comprehensive view of an IT environment complete with dependencies, observability solutions can show teams the “what,” “where” and “why” of any system event, and how the event might affect the performance of the entire environment. They can also automatically discover new sources of telemetry that might emerge in the system (a new API call to software application, for example).

These features often dictate how DevOps teams implement application instrumentation, debugging processes and issue resolution. Many observability solutions also include machine learning (ML) and AIOps capabilities that help glean insights from the mountains of raw data modern IT environments create and triage issues based on severity.

Both monitoring and observability are essential to network and application management. However, they differ in several key ways:

Monitoring tracks a system’s performance over time, using KPIs to anticipate performance issues and alert IT teams to data deviations in real time. It is primarily focused on finding system problems and notifying stakeholders of anomalous system events. This makes monitoring best suited for static, well-understood networks with predictable workloads.

Observability uses telemetry data—including distributed tracing features—from every device and component on the network to create a clearer, more complete picture of overall network performance. Observability tools can conduct real-time root cause analyses in complex, dynamic IT environments. They identify slow or broken network components and provide alerts for preemptive fixes, helping teams understand what to monitor and how to address issues proactively.

Monitoring tools use specific metrics and logs to detect system errors, resource usage patterns and specific failure modes. They help teams identify "known knowns," which means that IT teams can only find issues they’ve already anticipated. Application performance monitoring software, for example, can indicate whether an application is online, offline or experiencing latency issues.  

Monitoring is a vital process that helps ensure that systems are functioning properly, but monitoring tools can’t provide the context necessary for in-depth fault detection and incident response.

Observability helps teams visualize the entire architecture, storing device configurations, integrating diverse data sources across the network, and enabling seamless data analysis. Observability tools enrich telemetry data with additional information about the network environment (topology, device roles and application dependencies, for instance) and correlate network data to reveal "unknown unknowns."  

Enhanced visibility and deeper insights enable IT teams to be proactive and take a more exploratory approach to network and application management.

Monitoring systems collect data on usage trends and performance, and use that data to reveal what is happening. But they can’t necessarily explain why problematic events are happening.

Observability tools use surface-level data, data from CI/CD pipelines and historical data to provide context and correlate seemingly unrelated system events. Correlation features help developers accurately identify the root cause of issues, both in real time and retrospectively.

Monitoring is limited by the predefined datasets established by IT teams. It can’t identify issues outside of what’s been programmed, so monitoring tools are often insufficient for managing dynamic environments.

Relying solely on monitoring tools means relying on siloed monitoring data, which requires teams to expend extra resources on data correlation and manual root cause analysis. Manual processes slow down issue resolution and increase the likelihood of service disruptions and outages.

Observability tools can map data interactions from dynamic, diverse data sources across cloud environments (such as hybrid and multicloud environments), on-premises infrastructure and third-party applications. They’re inherently adaptable, making them well-suited for the problem-solving demands of modern IT infrastructures.

And, with their automation and AIOps capabilities, observability platforms can scale alongside ecosystems, so teams can effectively manage their infrastructures as they expand.

Monitoring tools often visualize system data in dashboards that enable IT personnel to view key metrics in a centralized location. However, they can’t illustrate the origins of system errors. Monitoring tools instead leave predictive tasks and root cause analysis to human operators.

However, observability tools can create traversable maps that include system errors and their root causes, automating root cause analysis workflows and streamlining troubleshooting processes for IT teams.

Monitoring and observability work hand in hand to create a comprehensive framework for managing IT systems, optimizing network connectivity and maximizing architecture scalability.

Monitoring tools establish the foundation of observability by tracking telemetry data and other key metrics and alerting teams to performance deviations. If, for example, an application exceeds the established response time threshold, a monitoring solution generates an alert.

An observability tool then analyzes the telemetry data and any data correlations (such as recent deployments), adding contextual information and integrating data layers to determine the reason for the alert. It traces an app’s interactions with other services to discern whether it’s running slowly because of a database bug or network congestion.

Insights from observability can also help refine monitoring capabilities, creating a feedback loop for continuous improvement. When the observability tool senses a change in data patterns, it can update monitoring alerts to reflect the new pattern so that monitoring and observability tools are working in lockstep.

Furthermore, observability tools use artificial intelligence (AI) and ML to maximize the potential of monitoring data. AI-driven observability features can use predictive analytics to forecast bottlenecks or failures (by using memory usage trends to predict server exhaustion, for example). And by using ML algorithms, observability tools can refine alerting practices, differentiating between critical alerts and noise.

If there’s a temporary—but expected—spike in CPU usage, for example, an observability solution can suppress the alerts generated by monitoring tools. However, if there’s an unanticipated, persistent spike in CPU usage the solution can help ensure the alert reaches the relevant IT personnel immediately.

Monitoring and observability serve as essential, complementary tools for optimizing application performance management (APM) and ITOps practices. Together, they support both proactive and reactive problem-solving practices across use cases and help ensure that businesses can provide users with the fast, high-availability IT services they’ve come to expect.