Monitoring vs observability: What’s the difference?

Monitoring once provided straightforward insights into IT health: you collected data, identified metrics to monitor, and diagnosed issues as they arose. However, as IT infrastructure evolves with cloud, containerization, and distributed architectures, traditional monitoring can struggle to keep pace. Enter observability, a methodology that not only enhances visibility but also enables proactive issue detection and troubleshooting.

Is observability simply a buzzword, or does it represent a fundamental shift in IT operations? This article will explore the differences between monitoring and observability, their complementary roles, and why observability is essential for today’s IT teams.

In this blog, we’ll cover: 

• What is monitoring?
• What is observability?
• Key differences between monitoring vs. observability
• How observability and monitoring work together
• Steps for transitioning from monitoring to full observability

What is monitoring?

Monitoring is the practice of systematically collecting and analyzing data from IT systems to detect and alert on performance issues or failures. Traditional monitoring tools rely on known metrics, such as CPU utilization or memory usage, often generating alerts when thresholds are breached. This data typically comes in the form of time-series metrics, providing a snapshot of system health based on predefined parameters.

Key characteristics of monitoring:

• Reactive by mature: Monitoring often triggers alerts after an issue has already impacted users.
• Threshold-based alerts: Notifications are generated when metrics exceed specified limits (e.g., high memory usage).
• Primary goal: To detect and alert on known issues to facilitate quick response.

An example of monitoring is a CPU utilization alert that may notify you that a server is under load, but without additional context, it cannot identify the root cause, which might reside elsewhere in a complex infrastructure.

What is observability?

Observability goes beyond monitoring by combining data analysis, machine learning, and advanced logging to understand complex system behaviors. Observability relies on the three core pillars—logs, metrics, and traces—to provide a holistic view of system performance, enabling teams to identify unknown issues, optimize performance, and prevent future disruptions. 

Key characteristics of observability:

• Proactive approach: Observability enables teams to anticipate and prevent issues before they impact users.
• Unified data collection: Logs, metrics, and traces come together to offer deep insights into system behavior.
• Root cause analysis: Observability tools leverage machine learning to correlate data, helping identify causation rather than just symptoms.

An example of observability: In a microservices architecture, if response times slow down, observability can help pinpoint the exact microservice causing the issue, even if the problem originated from a dependency several layers deep. 

For a deeper understanding of what observability entails, check out our article, What is O11y? Observability explained. 

Key differences of monitoring vs. observability

Monitoring and observability complement each other, but their objectives differ. Monitoring tracks known events to ensure systems meet predefined standards, while observability analyzes outputs to infer system health and preemptively address unknown issues.

Aspect	Monitoring	Observability
Purpose	To detect known issues	To gain insight into unknown issues and root causes
Data focus	Time-series metrics	Logs, metrics, traces
Approach	Reactive	Proactive
Problem scope	Identifies symptoms	Diagnoses causes
Example use case	Alerting on high CPU usage	Tracing slow requests across microservices

Monitoring vs. observability vs. telemetry vs. APM

Monitoring and observability are not interchangeable terms, but they do work together to achieve a common goal. Monitoring is an important aspect of an observability workflow, as it allows us to track the state of our systems and services actively. However, monitoring alone cannot provide the complete picture that observability offers.

Observability encompasses both monitoring and telemetry as it relies on these components to gather data and analyze it for insights into system behavior. Telemetry provides the raw data that feeds into the analysis process, while monitoring ensures that we are constantly collecting this data and staying informed about any changes or issues in our systems. Without telemetry and monitoring, observability cannot exist.

Application Performance Monitoring (APM) tools give developers and operations teams real-time insights into application performance, enabling quick identification and troubleshooting of issues. Unlike traditional monitoring, APM offers deeper visibility into application code and dependencies.

How monitoring and observability work together

Monitoring and observability are complementary forces that, when used together, create a complete ecosystem for managing and optimizing IT systems. Here’s a step-by-step breakdown of how these two functions interact in real-world scenarios to maintain system health and enhance response capabilities.

Monitoring sets the foundation by tracking known metrics

Monitoring provides the essential baseline data that observability builds upon. Continuously tracking known metrics ensures that teams are alerted to any deviations from expected performance.

• Example: Monitoring tools track key indicators like CPU usage, memory consumption, and response times. When any of these metrics exceed set thresholds, an alert is generated. This serves as the initial signal to IT teams that something may be wrong.

Observability enhances monitoring alerts with contextual depth

Once monitoring generates an alert, observability tools step in to provide the necessary context. Instead of simply reporting that a threshold has been breached, observability digs into the incident’s details, using logs, traces, and correlations across multiple data sources to uncover why the alert occurred.

• Example: If monitoring triggers an alert due to high response times on a specific service, observability traces can reveal dependencies and interactions with other services that could be contributing factors. Analyzing these dependencies helps identify whether the latency is due to a database bottleneck, network congestion, or another underlying service.

Correlating data across monitoring and observability layers for faster troubleshooting

Monitoring data, though essential, often lacks the detailed, correlated insights needed to troubleshoot complex, multi-service issues. Observability integrates data from various layers—such as application logs, user transactions, and infrastructure metrics—to correlate events and determine the root cause more quickly.

• Example: Suppose an e-commerce application shows a spike in checkout failures. Monitoring flags this with an error alert, but observability allows teams to correlate the error with recent deployments, configuration changes, or specific microservices involved in the checkout process. This correlation can show, for instance, that the issue started right after a specific deployment, guiding the team to focus on potential bugs in that release.

Machine learning amplifies alert accuracy and reduces noise

Monitoring generates numerous alerts, some of which are not critical or might even be false positives. Observability platforms, particularly those equipped with machine learning (ML), analyze historical data to improve alert quality and suppress noise by dynamically adjusting thresholds and identifying true anomalies.

• Example: If monitoring detects a temporary spike in CPU usage, ML within the observability platform can recognize it as an expected transient increase based on past behavior, suppressing the alert. Conversely, if it identifies an unusual pattern (e.g., sustained CPU usage across services), it escalates the issue. This filtering reduces noise and ensures that only critical alerts reach IT teams.

Observability enhances monitoring’s proactive capabilities

While monitoring is inherently reactive—alerting when something crosses a threshold—observability takes a proactive stance by identifying patterns and trends that could lead to issues in the future. Observability platforms with predictive analytics use monitoring data to anticipate problems before they fully manifest.

• Example: Observability can predict resource exhaustion in a specific server by analyzing monitoring data on memory usage trends. If it detects a steady increase in memory use over time, it can alert teams before the server reaches full capacity, allowing preventive action.

Unified dashboards combine monitoring alerts with observability insights

Effective incident response requires visibility into both real-time monitoring alerts and in-depth observability insights, often through a unified dashboard. By centralizing these data points, IT teams have a single source of truth that enables quicker and more coordinated responses.

• Example: In a single-pane-of-glass dashboard, monitoring data flags a service outage, while observability insights provide detailed logs, traces, and metrics across affected services. This unified view allows the team to investigate the outage’s impact across the entire system, reducing the time to diagnosis and response.

Feedback loops between monitoring and observability for continuous improvement

As observability uncovers new failure modes and root causes, these insights can refine monitoring configurations, creating a continuous feedback loop. Observability-driven insights lead to the creation of new monitoring rules and thresholds, ensuring that future incidents are detected more accurately and earlier.

• Example: During troubleshooting, observability may reveal that a certain pattern of log events signals an impending memory leak. Setting up new monitoring alerts based on these log patterns can proactively alert teams before a memory leak becomes critical, enhancing resilience.

Key outcomes of the monitoring-observability synergy

Monitoring and observability deliver a comprehensive approach to system health, resulting in:

• Faster issue resolution: Monitoring alerts IT teams to problems instantly, while observability accelerates root cause analysis by providing context and correlations.
• Enhanced resilience: Observability-driven insights refine monitoring rules, leading to more accurate and proactive alerting, which keeps systems stable under increasing complexity.
• Operational efficiency: Unified dashboards streamline workflows, allowing teams to respond efficiently, reduce mean time to resolution (MTTR), and minimize service disruptions.

In short, monitoring and observability create a powerful synergy that supports both reactive troubleshooting and proactive optimization, enabling IT teams to stay ahead of potential issues while maintaining high levels of system performance and reliability.

Steps for transitioning from monitoring to observability

Transitioning from traditional monitoring to a full observability strategy requires not only new tools but also a shift in mindset and practices. Here’s a step-by-step guide to help your team make a seamless, impactful transition:

1. Begin with a comprehensive monitoring foundation

Monitoring provides the essential data foundation that observability needs to deliver insights. Without stable monitoring, observability can’t achieve its full potential.

Set up centralized monitoring to cover all environments—on-premises, cloud, and hybrid. Ensure coverage of all critical metrics such as CPU, memory, disk usage, and network latency across all your systems and applications. For hybrid environments, it’s particularly important to use a monitoring tool that can handle disparate data sources, including both virtual and physical assets.

2. Leverage log aggregation to gain granular visibility

Observability relies on an in-depth view of what’s happening across services, and logs are critical for this purpose. Aggregated logs allow teams to correlate patterns across systems, leading to faster root cause identification.

Choose a log aggregation solution that can handle large volumes of log data from diverse sources. This solution should support real-time indexing and allow for flexible querying. Look for tools that offer structured and unstructured log handling so that you can gain actionable insights without manual log parsing.

3. Add tracing to connect metrics and logs for a complete picture

In distributed environments, tracing connects the dots across services, helping to identify and understand dependencies and causations. Tracing shows the journey of requests, revealing delays and bottlenecks across microservices and third-party integrations.

Adopt a tracing framework that’s compatible with your existing architecture, such as OpenTelemetry, which integrates with many observability platforms and is widely supported. Configure traces to follow requests across services, capturing data on latency, error rates, and processing times at each stage.

4. Introduce machine learning and AIOps for enhanced anomaly detection

Traditional monitoring relies on static thresholds, which can lead to either missed incidents or alert fatigue. Machine learning (ML) in observability tools dynamically adjusts these thresholds, identifying anomalies that static rules might overlook.

Deploy an AIOps (Artificial Intelligence for IT Operations) platform that uses ML to detect patterns across logs, metrics, and traces. These systems continuously analyze historical data, making it easier to spot deviations that indicate emerging issues.

5. Establish a single pane of glass for unified monitoring and observability

Managing multiple dashboards is inefficient and increases response time in incidents. A single pane of glass consolidates monitoring and observability data, making it easier to identify issues holistically and in real-time.

Choose a unified observability platform that integrates telemetry (logs, metrics, and traces) from diverse systems, cloud providers, and applications. Ideally, this platform should support both real-time analytics and historical data review, allowing teams to investigate past incidents in detail.

6. Optimize incident response with automated workflows

Observability is only valuable if it shortens response times and drives faster resolution. Automated workflows integrate observability insights with incident response processes, ensuring that the right people are alerted to relevant, contextualized data.

Configure incident response workflows that trigger automatically when observability tools detect anomalies or critical incidents. Integrate these workflows with collaboration platforms like Slack, Teams, or PagerDuty to notify relevant teams instantly.

7. Create a feedback loop to improve monitoring with observability insights

Observability can reveal recurring issues or latent risks, which can then inform monitoring improvements. By continually refining monitoring based on observability data, IT teams can better anticipate issues, enhancing the reliability and resilience of their systems.

Regularly review observability insights to identify any new patterns or potential points of failure. Set up recurring retrospectives where observability data from recent incidents is analyzed, and monitoring configurations are adjusted based on lessons learned.

8. Communicate observability’s impact on business outcomes

Demonstrating the tangible value of observability is essential for maintaining stakeholder buy-in and ensuring continued investment.

Track key performance indicators (KPIs) such as MTTR, incident frequency, and system uptime, and correlate these metrics with observability efforts. Share these results with stakeholders to highlight how observability reduces operational costs, improves user experience, and drives revenue.

Embrace the power of observability and monitoring

Observability is not just an extension of monitoring—it’s a fundamental shift in how IT teams operate. While monitoring is essential for tracking known issues and providing visibility, observability provides a deeper, proactive approach to system diagnostics, enabling teams to innovate while minimizing downtime.

To fully realize the benefits of observability, it’s important to combine both monitoring and observability tools into a cohesive, holistic approach. By doing so, businesses can ensure that their systems are not only operational but also resilient and adaptable in an ever-evolving digital landscape.