Defining Monitoring and Observability
Monitoring focuses on tracking known metrics to detect specific, predefined issues and alert you when they occur, acting as a reactive system with a limited scope. Observability goes beyond this by enabling you to deeply investigate and understand unknown problems and system behaviors by gathering and correlating diverse data types (logs, metrics, traces) to determine root causes, offering a proactive, holistic view.
A highly observable system provides visibility, enabling teams to explore the “unknown unknowns”, or problems that were not foreseen during the design or deployment phases. This means observability platforms must gather and correlate a wide range of data to support flexible queries and real-time analysis.
Key aspects of monitoring:
- Scope: Tracks predefined performance indicators and issues, focusing on “known-knowns” – problems you expect to see.
- Function: Acts as an early warning system, alerting you when specific thresholds or conditions are met, like a car’s dashboard warning lights.
- Data: Gathers specific, aggregated data points, often presented on dashboards.
- Approach: Reactive; it alerts you when something is wrong, based on known issues.
Key aspects of observability:
- Scope: Aims for a comprehensive understanding of the entire system, including its interactions and dependencies.
- Function: Provides context and insights into why an issue is occurring, enabling deeper diagnostics and root-cause analysis, even for novel problems.
- Data: Uses a combination of logs, metrics, and traces to provide a holistic view and enable correlation of data from different parts of the system.
- Approach: Proactive and investigative; it helps you understand the “why” and “how” of a problem, providing a deeper layer of analysis that builds on monitoring data.
This is part of a series of articles about network monitoring.
How Observability Works
Observability works by collecting three core types of telemetry data: logs, metrics, and traces. Logs capture discrete events and contextual details during application execution. Metrics provide quantitative measurements over time, like request rates or memory usage. Traces follow individual requests as they traverse services, recording latency and service interactions. Together, these data types are ingested into a telemetry backend that supports high-cardinality indexing and cross-dimensional correlation across service, endpoint, and user dimensions.
To make this data actionable, observability platforms provide powerful query interfaces and visualization tools. Engineers can slice and filter telemetry using high-dimensional queries to isolate anomalies or trace dependencies. For example, when latency spikes, they can filter traces by endpoint, drill into slow spans, correlate with logs, and identify backend delays. This correlation across telemetry types connects symptoms (e.g., high latency) to root causes (e.g., slow database queries) through real-time exploration rather than static dashboards.
Learn more in our detailed guide to network observability.
How Monitoring Works
Monitoring systems collect and aggregate specific metrics from applications and infrastructure. These metrics are usually numerical values emitted at regular intervals, such as CPU load, request rates, or error counts. The monitoring agent or exporter sends this data to a time-series database where it is stored for analysis and alerting. Dashboards visualize current and historical values, making trends and anomalies easy to spot at a glance.
Alerting in monitoring is based on static thresholds or simple rules. For instance, an alert might trigger if memory usage exceeds 80% for five minutes. Monitoring tools evaluate these rules in real time and push alerts via messaging or incident management platforms. The key to monitoring’s effectiveness lies in selecting the right metrics and tuning thresholds to avoid both false positives and missed incidents. While this approach is efficient for catching known problems, it lacks the flexibility to investigate novel issues without predefined metrics or thresholds.
What Are the Similarities Between Observability and Monitoring?
Both observability and monitoring aim to improve the reliability and performance of systems by providing visibility into how they operate. Each approach collects data from running applications and infrastructure, then transforms that data into insights that help engineers detect, understand, and resolve issues.
They also rely on familiar data sources: logs, metrics, and traces. While observability emphasizes flexible exploration, and monitoring emphasizes predefined tracking, both depend on the same underlying telemetry to produce actionable information.
Another similarity is their shared role in incident response workflows. Monitoring provides the initial alert when something goes wrong, and observability helps teams dig deeper into the cause. Together, they reduce downtime, improve mean time to resolution (MTTR), and support continuous system improvement.
Observability vs. Monitoring: The Key Differences
1. Purpose and Goals
The primary goal of monitoring is to ensure that systems and applications remain within expected operational parameters. It is built around predefined objectives, such as keeping latency below a threshold, ensuring CPU utilization stays within safe limits, or verifying that error rates do not exceed acceptable levels. Monitoring tools continuously check these conditions and raise alerts when something falls outside the defined boundaries.
Observability has a broader and more exploratory purpose. Instead of focusing solely on verifying conditions, it aims to provide a deep understanding of system behavior under all circumstances, including those unforeseen. The goal of observability is to enable engineers to ask new questions in real time, investigate unexpected issues, and uncover hidden dependencies across components.
2. Scope and Depth
Monitoring has a narrow scope because it operates on a limited set of predefined indicators. Typical monitoring dashboards display metrics such as CPU usage, memory consumption, request throughput, and service uptime. These are high-level signals that indicate whether the system is healthy, but they do not reveal much detail about what is happening within it. Monitoring provides breadth in terms of coverage across multiple components, but it lacks depth for drilling into complex interactions.
Observability expands the scope and adds significant depth. It integrates data from logs, metrics, and traces to create a multidimensional view of system behavior. Instead of just showing that latency is high, observability allows engineers to trace a specific request through multiple microservices, examine contextual logs, and correlate events to identify the root cause. This depth is crucial in distributed systems where problems may arise from interactions between services.
3. Reactivity vs. Proactivity
Monitoring is inherently reactive. It is designed to notify teams when a threshold is crossed or a known condition is violated. For example, if CPU usage exceeds 90% for more than five minutes, monitoring tools will send an alert. While this is effective for quickly catching common issues, it means that monitoring only responds to problems after they have started impacting the system.
Observability shifts the focus toward proactive exploration. By enabling engineers to query rich telemetry data in real time, observability enables the detection of subtle anomalies and emerging issues before they escalate into outages. For example, instead of waiting for CPU usage to reach 90%, observability tools might help identify unusual request patterns, memory leaks, or slow database queries early on.
4. Data Use
Monitoring typically relies on aggregated, low-cardinality data, such as averages, percentiles, or counters. This makes dashboards easy to interpret and alerts efficient to process, but also strips away detail. For instance, a monitoring dashboard might show that average latency has increased, but it cannot easily reveal whether the slowdown is isolated to a specific customer, endpoint, or region.
Observability systems are built to handle high-cardinality and high-dimensional data. They preserve granularity, allowing engineers to filter by user ID, region, or service, and to run ad hoc queries that reveal detailed patterns. Instead of showing only that latency is high, observability enables drilling into traces to see which service call or database query caused the delay. This richer data usage provides the context needed for deep debugging and precise root cause analysis.
5. Known vs. Unknown Failure Modes
Monitoring excels at handling known failure modes. These are scenarios where engineers already understand what can go wrong and can define metrics and thresholds to catch the issue. Examples include a disk reaching 95% capacity, a service returning 500 errors, or network latency exceeding a set limit. Monitoring is optimized for these predictable problems because the symptoms are easy to measure and the alerts are straightforward to configure.
Observability is built to address unknown failure modes. These are unexpected problems that lack predefined thresholds or clear metrics. For example, a subtle performance degradation might only appear under specific user workloads, or a cascading failure might result from complex interactions across microservices. In such cases, monitoring cannot help because the issue was not anticipated. Observability tools enable engineers to investigate these scenarios by allowing them to flexibly explore data, correlate signals, and discover root causes.
How Monitoring and Observability Work Together
Monitoring and observability are not competing approaches but complementary layers in a complete operations strategy. Monitoring establishes guardrails by detecting when systems deviate from expected behavior. It delivers fast, reliable alerts for known failure modes, ensuring that teams are notified quickly when outages or performance regressions occur.
Observability extends this by providing the tools to investigate why those alerts were triggered. Once monitoring signals that a threshold has been breached, observability enables engineers to drill into telemetry data, follow traces across services, and correlate metrics with logs. This combined workflow shortens the path from detection to diagnosis, reducing downtime and recovery times.
Together, they form a feedback loop. Monitoring handles the “what” by identifying symptoms, while observability addresses the “why” by uncovering root causes. When integrated into the same platform, they also reinforce each other: monitoring alerts can link directly to observability queries, and observability insights can inform better monitoring thresholds.
Observability and Monitoring: Modern Tools and Technologies
OpenTelemetry and Open Standards
OpenTelemetry has become the de facto open standard for collecting telemetry data across distributed systems. It defines vendor-neutral APIs and SDKs for generating, processing, and exporting logs, metrics, and traces. This standardization removes the need for custom instrumentation tied to a single platform, making it easier to switch tools without losing visibility.
By unifying telemetry under one framework, OpenTelemetry simplifies data correlation across services written in different languages or running in various environments. It also integrates with existing monitoring and observability platforms, allowing teams to centralize data pipelines.
Observability Platforms vs. Specialized Tools
Observability platforms provide end-to-end visibility by combining logs, metrics, and traces into a single workflow. They enable engineers to move seamlessly from high-level monitoring dashboards to detailed traces and contextual logs for root-cause analysis. Platforms typically include storage backends, query engines, visualization layers, and alerting systems, all integrated to support both monitoring and observability needs.
Specialized tools focus on one area of telemetry. Metrics systems like Prometheus excel at time-series storage and alerting, while log aggregation tools like Elasticsearch target large-scale text search. Tracing tools focus on distributed request flows. These specialized tools are lightweight and flexible but require additional effort to stitch together into a coherent workflow.
AI-Driven Anomaly Detection
AI and machine learning are increasingly applied to observability and monitoring to detect anomalies that static thresholds would miss. Instead of relying on fixed rules, anomaly detection models learn the typical patterns of system behavior and flag deviations in real time. This helps identify issues like gradual performance regressions, unusual traffic spikes, or resource leaks before they trigger outages.
AI-driven approaches also reduce alert fatigue by filtering out noise. Instead of sending alerts for every metric fluctuation, they prioritize signals that are likely correlated with real incidents. Some systems extend this further with automated root cause analysis, suggesting probable causes or even remediations based on historical incident data.
Related content: Read our guide to network telemetry.
Achieving Network Observability with Selector
Selector bridges the gap between monitoring and observability by unifying metrics, logs, events, and topology within a single, AI-driven platform. Unlike traditional monitoring tools that focus solely on thresholds and dashboards, Selector enables teams to explore complex, multi-domain environments with real-time correlation, root cause analysis, and context-rich insights across infrastructure, applications, and networks.
Powered by purpose-built machine learning and network-trained large language models (LLMs), Selector continuously ingests telemetry at scale to identify anomalies, uncover dependencies, and explain why issues occur — not just what went wrong. This eliminates the manual effort of cross-referencing siloed tools, helping teams move seamlessly from detection to understanding and resolution.
Selector’s observability framework transforms monitoring data into actionable intelligence. By providing both high-level visibility and deep contextual analysis, it empowers ITOps and NetOps teams to anticipate failures, validate performance baselines, and automate response workflows across hybrid and multi-cloud environments.
With Selector, organizations can:
- Correlate telemetry across domains to instantly connect symptoms to root causes.
- Automate insight generation through AI and LLM-powered anomaly detection and summarization.
- Unify observability and monitoring within a single platform for faster troubleshooting.
- Enhance collaboration among Dev, Sec, and Ops teams by sharing data and contextual narratives.
Selector redefines observability as an active, intelligent process — turning fragmented monitoring signals into cohesive operational awareness that drives resilience and performance.
Learn more about how Selector’s AIOps platform can transform your IT operations.