What’s the Difference Between Observability and Monitoring?

What’s the difference between observability and monitoring?

In DevOps, observability and monitoring are two distinct data-based processes. You use them to successfully maintain and manage the health and performance of distributed microservice architectures and their infrastructure. Distributed systems work by exchanging data between tens to hundreds or thousands of different components. 

Monitoring is the process of collecting data and generating reports on different metrics that define system health. Observability is a more investigative approach. It looks closely at distributed system component interactions and data collected by monitoring to find the root cause of issues. It includes activities like trace path analysis, a process that follows the path of a request through the system to identify integration failures. Monitoring collects data on individual components, and observability looks at the distributed system as a whole.

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How they work: observability vs. monitoring

Observability and monitoring are both essential processes in running effective DevOps programs.


Monitoring computing systems is a practice as old as running computing systems themselves. The monitoring process collects data about a system to check if the system is operating as expected. It includes reports and alerts on errors, faults, or anomalous data values. 

For instance, monitoring tools can collect data to measure the time taken to deploy an application release. If the time taken falls outside an expected window, the monitoring tools can alert users, indicating that something has likely gone wrong. 

DevOps monitoring covers the full software development lifecycle (SDLC). Application performance monitoring (APM) is a specialized subset of DevOps monitoring that focuses on applications running in production. It prioritizes metrics that apply to user experience.

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Observability brings a wider scope and visibility to traditional monitoring tools, incorporating extra situational and historical data and system interactions. It enables investigation into the root cause of monitoring alerts, alongside the ability to investigate issues that arise due to multi-component interactions.

You can use observability tools to debug distributed application architecture-based systems themselves. You can also use them to observe the real-time health of the system overall and the interactions between system components. You can use observability software to map an entire interconnected system, its dependencies, and real-time interactions.

What are the similarities between observability and monitoring?

Both observability and monitoring originally derive from the field of control theory, a system engineering and mathematical field. Both are used extensively throughout computing and computing-blended physical environments for maintenance of system health and performance. In DevOPs, the terms are often used interchangeably because both relate to telemetry data like metrics, events, logs, and traces.


Metrics are system data measurements. For instance, a metric could be network throughput or the number of application errors in a week. Monitoring reports on metrics and observability looks for ways to improve their values.


Events are discrete actions that occur in a system at any point in time. An example might be a user changing a password or an alert indicating a high number of password attempts. Events trigger monitoring and support observability in investigating incidents.


Logs are software-generated files that contain information about the operations, activities, and usage patterns of the system. They include a historical record of all processes, events, and messages along with additional descriptive data, such as timestamps, to contextualize this information. Monitoring generates logs that observability uses for further system analysis.

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Traces are the full path of a single operation across its various interrelated systems. For fully distributed tracing, signals must be emitted from every transaction in the microservice architecture for tracking. Monitoring enables tracing, which is an important function of observability.

Observability vs. monitoring: key differences

Monitoring is a critical core component of observability. Comprehensive monitoring creates descriptive metrics, events, logs, and traces that measure what is essential in an easily identifiable and retrievable manner. Historic records are stored alongside current measurements to build a broad picture of the system. Observability can then use what monitoring creates to investigate incidents more deeply.

Monitoring is the when and what of a system error, and observability is the why and how. There are many signals to map and monitor to gain an overall picture of the entire system’s internal state and health. You need all of this data to be able to conduct effective investigations. For observability to be useful and effective, monitoring must be comprehensive and descriptive.


With monitoring systems, you can discover anomalies or unusual behavior in system state and performance. With observability, you can further investigate any anomalies, even if they occur because of the interactions between hundreds of service components.

Cause and effect

Monitoring focuses on measuring some value or values to see whether there is an effect on a system. The goal of observability is to understand the cause of that effect. For example, when new code is released, monitoring tracks system metrics to see if application load times or data retrieval times are affected by the change. In case of impact, observability investigates the reason or cause. It answers which part of the code change caused the effect and suggests ways to fix it.

System interactions

Monitoring typically measures the health of a particular system. It collects data on all of the different system components, but the data might be isolated, and the interrelation is difficult to understand. With observability, you gain an overall view of all of the interrelated systems to get an understanding of where and how problems are happening.

When to use: observability vs. monitoring

Retrospective error-catching, such as learning of outages from users or finding an application is running on the wrong target system, can result in lost time, money, reputation, and developer resources. Monitoring is a must-have for proactive error-catching. Monitoring tools raise alerts for all types of discrepancies that you can identify and fix before they cause long-term consequences.

An observable system adds to existing monitoring capabilities. It’s essential in running microservice application architectures, especially when they’re deployed to distributed cloud infrastructure. With monitoring alone, it becomes near-impossible to identify and isolate the application or service where errors start. The right data capture and monitoring, coupled with observability, makes it possible to trace errors through complex systems.

Summary of differences: monitoring vs. observability




What is it?

Measuring and reporting on specific metrics within a system, to ensure system health.

Collecting metrics, events, logs, and traces to enable deep investigation into health concerns across distributed systems with microservice architectures.

Main focus

Collect data to identify anomalous system effects.

Investigate the root cause of anomalous system effects.

Systems involved

Typically concerned with standalone systems.

Typically concerned with multiple, disparate systems.


Limited to the edges of the system.

Available where signals are emitted across disparate system architectures.

System error findings

The when and what.

The why and how.

How can AWS help with your observability and monitoring requirements?

AWS Cloud Operations provides a model and tools for a secure and efficient way to operate in the cloud. You can transform your organization, modernize and migrate your applications, and accelerate innovation with Amazon Web Services (AWS). 

With monitoring and observability in cloud operations, you can collect, correlate, aggregate, and analyze telemetry. This applies across your network, infrastructure, and applications in the cloud, hybrid, or on-premises environments. You can gain insights into your system's behavior, performance, and health. With these insights, you can detect, investigate, and remediate problems faster. When coupled with artificial intelligence (AI) and machine learning (ML), you can wield these insights to proactively react, predict, and prevent problems.

For example, you can use:

  • AWS X-Ray to analyze and debug production and distributed applications, trace user requests, identify bottlenecks, and monitor performance
  • Amazon CloudWatch to access and analyze resource and application data and external outputs using powerful visualization tools on AWS, on premises, and in other clouds
  • Amazon Managed Grafana to fully manage Grafana (the popular monitoring tool) for querying, visualization, and alerting on metrics, logs, and traces across operational data
  • Amazon Managed Service for Prometheus to fully manage Prometheus, a container-monitoring tool for maintaining and querying time series metrics from your self-managed Kubernetes clusters of containers

Get started with monitoring and observability on AWS by creating an account today.