Observability vs Monitoring: What’s the Difference?

When problems arise in an application, customers are affected, and ultimately the business is affected as well. IT teams scramble to discover the root cause of the problems and solve them quickly. This is complicated by increasingly complex and distributed cloud architectures that provide application services. That’s where monitoring and observability come in to identify the underlying cause of a problem.

Observability tends to be used as another word for monitoring. Frequently, these two terms are often used interchangeably but they are not the same, and beginners to IT infrastructure and software development are often confused between the two. That’s because both have the same objective of maintaining system health, yet have different purposes, approaches and scope in how they tackle the issue.

So what’s the difference between observability and monitoring, and how do you know which better suits your organizational needs? Do you need observability, or is monitoring enough? We’ll look at how each works and help you reach the answer to these questions.

Monitoring: The Traditional Approach

What is Monitoring?

Monitoring is the process of collecting, ingesting, and analyzing aggregate data from IT systems to assess the health of systems. Examples of data are application, infrastructure, and/or cloud telemetry data. It’s been the go-to solution for keeping systems running smoothly for years.

Monitoring relies on predefined metrics, such as CPU or memory usage and network traffic, logs, and traces. Examples include checking for server status, network latency, response times, disk and CPU usage. This data enables IT teams to track the performance and availability of their infrastructure and applications in real time. Monitoring tools and platforms can provide dashboards and alerts and have reporting capabilities to help IT teams monitor components, identify predicted issues, and troubleshoot problems that arise in given environments.

Where monitoring truly shows its value is in analyzing long-term trends and alerting. It shows you not only how the app is functioning, but also how it’s being used over time.

What do you use Monitoring for?

Current web applications use several types of monitoring, such as Infrastructure Monitoring, Synthetic monitoring and Real User Monitoring (RUM). Here’s what these types of monitoring are used for:

• Infrastructure Monitoring tracks immediate metrics regarding the current health of your IT system, such as server health, uptime, and resource utilization.
• Synthetic Monitoring is generally used to monitor short-term trends, using automation tools to measure a system’s functionality. For example, it will use sample values to decide if a web application is performing as expected.
• Real User Monitoring (RUM) is more suited for monitoring long-term trends, and involves recording the user’s actual interaction with the application and finding out if the application is performing or functioning as expected.

Limitations of Monitoring

While monitoring is useful, it comes with significant limitations. Imagine that your IT environment is a patient you’re diagnosing — monitoring reveals the symptoms of the patient, but these may not be enough to reveal the deeper cause of these symptoms and issues.

Monitoring is limited in that it:

• Doesn’t Detect Root Causes or Provide Solutions: Monitoring helps teams watch the system’s performance and send alerts when known failures are detected, but doesn’t tell you why the problem arises and how to fix it.
• Only Identifies ‘Known Unknowns’: Monitoring can only detect issues based on pre-defined conditions. You have to know what metrics and logs you need to track. If an issue that your team hasn’t predicted occurs, happening outside your pre-defined conditions, monitoring can’t catch it. This leads to missing key production failures and other problems.
• Can’t Perform Proactive Issue Resolution: Monitoring leads to reacting when pre-set error thresholds are crossed, but does not help predict problems before they occur, diagnose root causes, or proactively take measures to prevent problems from occurring.
• Unsuited for Modern Distributed Environments: Monitoring tools are traditionally siloed, and have limited efficiency in modern cloud architectures and larger, distributed environments. In essence, monitoring is often reactive and focused on finding the symptoms rather than solving the underlying problems.

Observability: A Modern Solution for Complex Systems

What is observability?

Observability is the ability to understand a complex system’s internal state based on external outputs, namely by analyzing the data it generates, such as logs, metrics and traces. When a system is observable, a user can identify the root cause of a performance problem from the data it produces without additional testing or coding.

An observability solution analyzes output data, provides an assessment of the system’s health and offers actionable insights for addressing the problem. This provides DevOps teams a holistic and unified view of the entire IT environment with context and understanding of interdependencies. Ultimately, teams can detect problems proactively and resolve issues faster, particularly in distributed systems.

Observability tools provide customizable dashboards, automation capabilities, analytics, and alerts that help teams perform root cause analysis faster and more effectively. A few platforms even take it a step further by correcting these issues themselves.

In a nutshell, observability is an evolving tool for improving the performance and resilience of modern IT operations and the services they manage. With improved resilience comes better productivity.

When Do You Need Observability?

Observability becomes increasingly important the more complex, unpredictable, and distributed your systems become. Some common use cases include:

• Cloud-Native Applications & Microservices: As your system becomes more distributed, with various services running in the cloud, or more than one cloud, monitoring everything manually in hybrid, cloud or multi-cloud environments becomes more difficult. With observability, you can track interactions across microservices regardless of where they’re hosted.
• Correlating Logs, Metrics, and Traces: Observability allows you to analyze and correlate logs, metrics, and traces in real time to get a more accurate picture of system health.

Key Benefits of Observability Over Monitoring

When comparing observability vs monitoring, here are the main advantages observability has over monitoring:

• Troubleshooting ‘Unknown Unknowns’: With observability, you can identify and troubleshoot issues that are difficult to predict, such as failures in complex, dynamic systems. These include problems that an IT team may not have anticipated might happen.
• Proactive Issue Prevention: Observability helps you identify issues before they escalate into critical problems, with the capability of taking proactive measures to prevent these issues before they even occur.
• Faster Debugging & Reduced MTTR (Mean Time to Resolution): With observability tools, you can trace issues across your entire stack and quickly identify the root cause, speeding up the debugging process.

Why Do Observability and Monitoring Seem Similar?

So, what leads to the confusion between observability and monitoring? For one, the terms themselves are similar, and both have similar end goals. Both aim to provide insights into the health, performance, and behavior of a system, with the same objective to improve system reliability and identify the cause of a problem to improve overall performance.

They also rely on the same data and use the same data collection, analysis, and visualization techniques to enable proactive detection and troubleshooting of issues. Ultimately, they empower engineers to ensure system reliability, performance optimization, and efficient resource utilization. Whether you’re looking to create an observable or monitored system, you need to first capture the right outputs. This requires installing collectors and agents, and possibly instrumenting application code.

The two tasks can also coexist. As previously mentioned, monitoring is a subset of observability. In fact, many observability platforms have monitoring tools baked into their interface. That means you don’t need two separate sets of tools to handle both monitoring and observability — it’s all included together.

Between observability and monitoring, which should you choose?

Having explained monitoring and observability in depth, we come to the key question: When it comes to observability vs monitoring — which wins? How do you know which model is best for your environment? Here’s a simple table to compare monitoring and observability directly.

• If your system is small and predictable → Monitoring is enough. A simple application or small infrastructure with minimal complexity can rely on monitoring to detect performance issues as they arise.
• If you run distributed, cloud-based applications → Observability is required. For microservices or systems that run across multiple clouds, observability offers the depth needed to understand interactions and diagnose problems.
• If you experience frequent outages and slow debugging → Adopt Observability. If your systems are unpredictable or face a high volume of incidents, observability can dramatically reduce downtime by providing better diagnostics.

How to Transition from Monitoring to Observability

If you’re currently relying on traditional monitoring but want to transition to observability, here’s a step-by-step guide:

Step 1: Identify Gaps in Your Current Monitoring Strategy

Assess where your current monitoring approach falls short. Are there issues you can’t trace back to their root causes? Is your team struggling to debug problems quickly?

Step 2: Implement Distributed Tracing & Log Correlation

Start by adding distributed tracing (for microservices) and log correlation to your system. This enables you to follow the path of requests and identify bottlenecks or failures more effectively.

Step 3: Choose an Observability Tool That Integrates with Your Stack

Look for observability tools that integrate well with your current infrastructure. Popular options include Prometheus, OpenTelemetry, and AWS CloudWatch. Ensure the tool supports and integrates with your cloud services, Kubernetes, and other technologies you use.

Step 4: Train Teams on Proactive Observability Techniques

Adopting observability requires a shift in mindset. Train your teams to use these tools not just to react to problems, but to prevent them by proactively analyzing data and optimizing system performance.

Conclusion

While monitoring helps detect issues, observability goes beyond monitoring. It helps you understand why those issues occur and how to fix them before they become bigger problems. If you’re operating in a traditional, small-scale environment, monitoring may be enough. However, as your systems scale and become more complex, observability becomes essential for maintaining high availability and performance.

If you’re ready to take the next step in improving system health, start by reviewing your current monitoring setup. Begin exploring observability tools today to gain deeper insights into your system and ensure smoother, more reliable operations. TrueWatch seamlessly integrates with the six largest global clouds and comes with a wide range of out-of-the-box functions and tools which are easy to use. No matter what you’re using, TrueWatch will slot easily into your IT stack. The future of system management is proactive, and you don’t want to be left behind.