Observability vs. Monitoring: Understanding Which Maintenance System You Should Use

When systems are running smoothly, it’s easy to overlook the processes that keep them that way. But as any IT operations professional knows, uptime doesn’t just happen. Behind every seamless user experience is a toolkit of monitoring and observability solutions working to detect, diagnose and fix issues before they become larger problems. While observability and monitoring are often used in the same sentence, they serve different purposes. Understanding the differences is the first step to optimizing your infrastructure.

Which one should you use and when? Let’s break down the core functions of observability and monitoring, how they overlap, where they diverge and how combining them can give you the visibility and control your team needs.

What are observability and monitoring?

Observability and monitoring are both used to track system health and performance. However, they do so in different ways, serving unique roles in modern IT operations.

Monitoring is about detection. It’s designed to let you know when something goes wrong by collecting and evaluating predefined metrics. You define what normal looks like, set thresholds for deviation and get alerts when those thresholds are crossed. It’s an effective way to stay ahead of known issues, such as high CPU usage, low memory or service downtime. It’s a reactive approach that’s built to catch specific, expected problems and notify you immediately.

Observability, on the other hand, is about understanding. It helps you answer, “What happened?” and “Why did it happen?” Observability brings together diverse telemetry to provide a comprehensive view of your systems. It allows you to explore patterns, detect anomalies and investigate incidents without relying solely on preconfigured alerts.

In practice, monitoring tells you something is broken. Observability helps you figure out what broke and how to fix it. That makes observability essential for troubleshooting complex, cloud-native environments where unexpected interactions and behaviors are common and where static monitoring alone may not be enough.

What is observability?

Observability is the ability to understand a system’s internal state based on its external outputs. Rather than simply alerting you when something breaks, observability allows you to investigate why it’s happening. It supports open-ended exploration, helping you answer questions like, “Why is this service running slow?” or “What’s causing intermittent database failures even though all metrics look fine?”

Unlike monitoring, observability doesn’t depend on predefined rules. Instead, it gives you flexible access to detailed telemetry data so you can explore system behavior as it unfolds. Logs offer granular event details, metrics show performance trends over time and traces follow the journey of individual requests across services. These three data types are commonly referred to as the “three pillars” of observability. They work together to help you understand complex, distributed systems at scale.

Observability is especially valuable in dynamic environments like Kubernetes, where service-to-service communication, auto-scaling and rapid deployments create more potential failure points. Modern observability platforms offer advanced capabilities, such as anomaly detection, predictive modeling and automated root cause analysis using AI/ML. These tools help teams detect issues faster and provide context to resolve them before users are affected.

Learn more about observability.

What is monitoring?

Monitoring is more structured and more prescriptive. It’s built around known problems, predefined metrics and clear thresholds. You decide in advance what to measure and what counts as a failure. This typically includes things like CPU utilization, memory consumption, disk space, request latency or error rates. These metrics help determine whether a system is operating within acceptable limits.

When one of those metrics crosses a critical threshold, the monitoring system sends an alert. That alert helps IT teams respond quickly to known failure conditions, such as a service going down, an API failing to respond or a load balancer becoming overwhelmed. The value of monitoring lies in its speed and specificity. It’s great for catching obvious, repeatable issues before they impact users.

However, monitoring assumes you already know what to track. In fast-changing environments like microservices or Kubernetes, that’s not always the case. You may see a spike in resource usage or a surge in restarts but not understand the cause. While Kubernetes monitoring provides key data, it doesn’t always offer the broader context needed for root cause analysis. That’s where observability becomes essential. It gives you the tools to explore deeper and troubleshoot more effectively.

Monitoring vs. observability: What do they have in common?

You’ll often hear observability and monitoring discussed together and for good reason. While they serve distinct roles, they work best as a pair. They complement each other by addressing different aspects of system visibility, stability and performance.

• System health insights: Both provide essential visibility into system uptime, latency, throughput and other performance metrics. This visibility helps teams maintain reliability across environments.
• Reliance on telemetry data: Whether it’s metrics, logs, traces or events, both rely on telemetry to understand how systems behave. This data forms the foundation for analysis and alerts.
• Goal of faster incident resolution: Speed matters. Both approaches are designed to reduce downtime, speed up response times and improve mean time to resolution (MTTR).
• Support for cloud-native environments: Today’s systems are complex. Both types of tools are designed for containerized, distributed and microservices-based architectures.
• Automation and alerting: Dashboards, rules and alerts are core to both practices.
• Integration with DevOps: Both are vital to continuous delivery, agile workflows and site reliability engineering.

Understanding the key differences between observability and monitoring

While they share some goals, observability and monitoring solve different problems. Here’s how they differ:

• Scope: Monitoring tells you that a problem exists. Observability helps you figure out why.
• Data depth: Monitoring works with predefined metrics. Observability uses logs, metrics and traces together to support open-ended exploration.
• Flexibility: Monitoring is ideal for known issues and static environments. Observability is better suited for dynamic, unpredictable systems.
• User intent: Monitoring asks, “Is everything working as expected?” Observability asks, “What’s going on inside the system?”
• Root cause analysis: Monitoring can alert you to symptoms. Observability guides you to the root cause.
• Scale: As systems grow more complex, observability becomes increasingly necessary to track interdependencies and diagnose performance issues.

Observability gives you the tools to navigate the unknown, which is critical in a Kubernetes environment. You might be alerted by your monitoring system that latency has increased. However, it’s observability that helps you understand whether that’s due to a networking issue, a failed container or a change in service traffic patterns.

A summary of observability vs. monitoring

If you’re looking for a quick way to compare observability and monitoring, this side-by-side table breaks down their key differences. While the two are often used together, they serve different purposes depending on your infrastructure needs.

Monitoring is great for catching what you already know to watch for. It’s structured, rules-based and helps you stay compliant with service level agreements (SLAs). Observability goes further. It lets you dive deep into system behavior when things get messy, unpredictable or unclear.

Think of monitoring as your early warning system, and observability as your investigative toolkit. Both are essential, but they work in different contexts. Whether you’re running a legacy system or managing microservices at scale, understanding how these two approaches compare can help you make better tooling and architecture decisions.

Here’s a breakdown of the major differences between observability and monitoring to help guide your strategy:

Which is better: Can observability and monitoring work together?

Short answer: yes. In fact, you’ll get the most value when you combine both.

Monitoring is essential for alerting you to critical failures in real time. It keeps your team informed and responsive. But it only works for the issues you expect. You define the metrics, thresholds and alerts ahead of time. If something breaks outside of those parameters, traditional monitoring may not catch it or may not give enough detail to resolve it quickly.

Observability fills in the gaps. It provides context and depth. This helps you troubleshoot complex systems, investigate intermittent bugs and gain confidence in new deployments. It’s built for the unknown. It allows you to explore system behavior without needing a preconfigured alert in place. This is especially useful in environments like Kubernetes, where services are constantly being spun up, scaled or updated.

Together, observability and monitoring create a full picture of system health. Monitoring handles the alerts and observability enables the analysis. One tells you something is wrong. The other helps you understand why and how to fix it.

Here’s when to use each:

• Use monitoring for tracking SLAs, alerting on failures and managing known performance baselines.
• Use observability when introducing new microservices, troubleshooting cascading failures or scaling distributed systems.

By layering the two, you create a more resilient and proactive operations environment: one that catches issues fast, solves them faster and adapts as your systems evolve.

Observability vs. monitoring use cases

While observability and monitoring work best together, there are distinct use cases where each excels. Understanding those differences helps you apply the right tool for the job.

Monitoring use cases typically involve known issues, predefined baselines and environments that don’t change often. Say, for example, your team manages a legacy system with predictable traffic patterns. Monitoring tools can track CPU usage, memory consumption or disk space. You can set thresholds to trigger alerts if performance dips or a service becomes unresponsive. This makes monitoring ideal for detecting outages, enforcing service level agreements (SLAs) or managing capacity in relatively static environments.

Observability use cases are better suited for dynamic, distributed systems like Kubernetes, serverless functions or microservices architectures. In these environments, problems often don’t follow predictable patterns. For example, you might see intermittent latency spikes that aren’t tied to any single metric. Observability tools allow you to dig deeper. You can examine logs, metrics and traces together to find hidden bottlenecks, misbehaving services or network anomalies.

Other strong observability use cases include debugging after a new deployment, troubleshooting cascading failures and analyzing user behavior across services. Observability is also key during incident response and postmortem reviews when you need to reconstruct what happened in real time.

In short, monitoring is about the expected. Observability is built for the unexpected. Use monitoring to track system health and trigger alerts. Use observability to explore what went wrong, how it happened and how to prevent it in the future.

Which tool should you choose for observability and monitoring?

SUSE offers capabilities for both monitoring and observability tailored for modern IT operations.

SUSE Rancher helps teams deploy, manage and secure Kubernetes clusters at scale. Built-in monitoring gives you immediate visibility into cluster health. But for more advanced use cases, SUSE offers integrations and solutions for observability across logs, metrics and traces.

SUSE Linux Enterprise Server provides a secure, scalable foundation for cloud-native observability tools. Whether you’re running workloads in the cloud, on-premises or at the edge, SUSE supports end-to-end visibility and control.

Our commitment to open-source flexibility means you’re never locked into a specific tool or vendor. You can integrate your favorite observability and monitoring solutions with ease, whether that’s Prometheus, Grafana, Fluentd or something custom.

Final thoughts on observability vs. monitoring

In a landscape where distributed architectures, edge workloads and real-time user experiences are the norm, observability and monitoring are must-haves.

Monitoring helps you stay responsive. Observability helps you stay curious. Together, they give you the operational insight needed to innovate, scale and maintain uptime with confidence.

Don’t wait for an outage to realize what your system is missing. Build a foundation that combines both approaches and gives your team the data clarity they need.

FAQs on observability vs. monitoring

What is telemetry?

Telemetry refers to the automated collection and transmission of data from systems or applications to a centralized platform. In IT, telemetry includes logs, metrics and traces used for observability and monitoring. This data helps teams understand how systems are performing and identify any anomalies or failures that require attention.

What is APM?

Application Performance Monitoring (APM) is a practice within monitoring that focuses specifically on tracking the performance and availability of software applications. APM tools help identify slow transactions, backend issues or outages by monitoring metrics like response times, error rates and throughput. Many modern APM tools also include observability features like distributed tracing and real-user monitoring.

Is monitoring a subset of observability?

Yes, monitoring is often considered a subset of observability. While monitoring focuses on predefined metrics and alerts, observability takes a broader view by collecting and analyzing diverse data types (like logs and traces) to provide a deeper understanding of system behavior. You can think of monitoring as one of the key tools used within a comprehensive observability strategy.

What are some examples of observability vs. monitoring in practice?

A monitoring tool might alert you that a server’s CPU usage has hit 95%. That’s useful, but it doesn’t explain why. Observability tools let you correlate that spike with a recent deployment, a memory leak or an upstream service change. In Kubernetes environments, you might monitor pod restarts. Observability helps trace the full request path to find the exact point of failure or latency.

Do I need observability if I already have good monitoring?

Yes. Even strong monitoring setups have limitations. They’re typically configured for known issues and rely on static thresholds. Observability provides flexibility for dynamic environments and unknown failure modes. It lets you explore and troubleshoot without needing every possible alert in place beforehand. If your infrastructure includes microservices, containers or hybrid cloud, observability becomes essential for understanding behavior across the entire stack (not just surface-level symptoms).