Modern systems are complex, distributed, and fast-changing, so keeping them reliable requires more than watching dashboards. Observability vs. Monitoring explains how teams gain the deep insight needed to detect, diagnose, and resolve issues.
Monitoring collects predefined metrics and alerts you to known problems, while observability provides rich, contextual telemetry to investigate unknown failures.
In this blog, we will break down what each means, how they differ, and how to bridge the gap between the two.
Table of Contents
Monitoring
Monitoring is the collection and analysis of predefined metrics (CPU usage, memory utilization, network latency, error rates) and logs. It is a reactive practice that checks if a system is operating as expected. It is used to detect known failures and prevent downtime by using established thresholds. Teams can refine this process with effective monitoring sensitivity strategies to reduce noise and catch critical issues faster.
Purpose
The purpose is to provide situational awareness. Teams use it to track key performance indicators (uptime, response time, throughput, and error rate) and receive alerts when things deviate from normal. It is focused on finding problems that are already anticipated.
Best for
Monitoring is best for traditional or monolithic applications. These systems have fewer moving parts and predictable failure modes. The environment is static and well-understood, making it easy to define what to measure.
Example
An e-commerce company uses a monitoring tool to track its legacy payment service. They set alerts for specific thresholds, including latency above 300 ms, error rate over 2%, and CPU usage above 85%. When latency exceeds the limit, the tool alerts the on-call engineer. This tells the team that a problem is occurring, but not what is causing it. The team can then start a manual investigation to find the root cause.
Observability
Observability is the ability to infer the internal state of a system from its external outputs. It is an exploratory and proactive practice for diagnosing unknown issues. It relies on rich, contextual telemetry data, including logs, metrics, and traces. These insights often tie into SLIs and SLOs, which help teams measure and maintain system reliability.
Purpose
The purpose is to provide a deep, contextual understanding. It helps teams pinpoint the root cause of issues in complex architectures. It answers the ‘why’ and ‘how’ behind system behavior, especially in microservices and cloud-native environments.
Best for
Observability is essential for modern, distributed systems. When teams don’t know how a system might fail, observability provides the tools to ask new questions and investigate on the fly. This makes it crucial for complex, dynamic environments where failure modes are unpredictable.
Example
The same e-commerce company uses an observability platform to monitor its microservice-based recommendation engine. The platform collects logs, metrics, and traces from services like the product catalog and inventory API. When customers report slow recommendations, the SRE team traces requests end-to-end and finds a new inventory service causing high latency. With this insight, they quickly fix the issue and restore normal performance without needing predefined alerts.
The Difference Between Observability and Monitoring
Monitoring tells you when something is wrong. Observability helps you find out why.
They work at different levels: Monitoring detects surface-level issues, and observability provides system-wide context.
Here’s a quick comparison:
| Aspect | Monitoring | Observability |
| Primary Purpose | Answers “Is the system working?” by tracking known metrics and alerts. | Answers “Why is it behaving this way?” using deeper, contextual data. |
| Approach | Reactive: finds problems after thresholds break. | Proactive: explores data to spot and prevent issues. |
| System Complexity | Works best for simple or well-understood systems. | Designed for complex, distributed systems, like microservices. |
| Data Sources | Uses predefined metrics and logs. | Uses logs, metrics, and traces for full visibility. |
| Instrumentation | Collects basic metrics externally (limited detail). | Uses in-code instrumentation for deeper insight. |
| Correlation & Context | Connects a few related metrics manually. | Automatically links data across services to show the root cause. |
| Problem Identification | Finds known issues like CPU spikes or downtime. | Detects unknown issues before they grow into outages. |
How They Work Together
Monitoring and observability aren’t rivals; they’re partners.
Monitoring and observability are most effective when used together to create a feedback loop for continuous improvement.
Monitoring alerts teams to known issues based on predefined metrics. Observability platforms then provide the context from logs, traces, and metrics needed to investigate and find the root cause. Insights gained can then be used to refine monitoring practices.
Conclusion
Without monitoring, failures go unnoticed. Without observability, teams can’t explain why failures occur.
With both, you can detect issues early, investigate faster, and recover confidently.
In today’s complex systems, observability and monitoring work best together as a safety net. Monitoring detects issues early, while observability provides the context to understand and resolve them quickly, keeping systems stable and teams confident.
FAQs
What are the three pillars of observability?
The three pillars of observability are logs, metrics, and traces. Metrics are numeric data points providing a high-level view. Logs are detailed event records. Traces follow request journeys in distributed systems.
What is monitoring and observation?
Monitoring is a reactive practice for tracking known issues. Observation, or observability, is the ability to understand a system’s internal state for deep investigation into unknown issues.
What is the difference between monitoring and visibility?
Monitoring is the process of collecting and analyzing data based on known metrics. Visibility is the insight gained from this data. And Observability is the practice of enabling visibility, particularly into unknown issues.
What are the 4 golden signals of observability?
The four golden signals are customer-centric metrics:
- Latency: How long a request takes.
- Traffic: The demand on your system.
- Errors: The rate of failed requests.
- Saturation: How full your system is.