What is Observability?
Observability refers to the ability to understand the internal state and behavior of applications and systems by examining their output data, such as logs, metrics, and traces. A system is considered observable when this data provides sufficient visibility to assess performance, detect issues, and understand how the system is behaving at a given point in time.
Key Takeaways
- Observability helps teams understand how complex software systems behave by providing contextual insight across applications, services, and infrastructure.
- By correlating telemetry signals, observability supports faster investigation of performance issues and unexpected system behaviour in distributed environments.
- Observability complements monitoring and APM by enabling deeper analysis of system interactions rather than relying only on predefined metrics or alerts.
Why Observability is Important?
Observability is essential for cross-functional teams to understand and manage complex, distributed systems. It helps identify performance bottlenecks and faulty components, enabling teams to address issues early and reduce the risk of user-facing disruptions.
In cloud-native environments, where systems continuously change, observability helps teams detect unexpected behaviors and respond to emerging challenges. It also supports AIOps initiatives by providing the data foundation needed for automation across the DevSecOps lifecycle.
Beyond technical benefits, observability data offers visibility into how digital services perform from a user and business perspective. This enables organizations to improve user experiences, assess the impact of software changes, and align system performance with business objectives.
Three Pillars of Observability
Metrics, logs, and traces are the core telemetry data types that support observability. Telemetry refers to the data generated by systems and applications to describe their behavior, performance, and operational state. Together, these signals provide visibility into modern, distributed environments.
1. Logs
Logs are time-stamped records that capture events and actions generated by applications and systems during runtime. They provide contextual information about execution flow, errors, warnings, and user activity, making them valuable for debugging, auditing, and incident investigation. Log data may include application logs, access logs, error logs, security logs, and transaction logs, each offering insights into specific system activities.
2. Metrics
Metrics are numerical values that represent system performance and resource usage over time. Collected at regular intervals, they may originate from infrastructure, applications, cloud services, or external dependencies. Metrics help teams track performance levels, establish baselines, identify abnormal behavior, and monitor trends related to system health and capacity.
3. Traces
Traces capture the complete path of a request as it moves through multiple services and components in a distributed system. By recording the timing and relationship between individual operations, traces help teams visualize request flows, identify performance bottlenecks, and diagnose latency issues. In microservices environments, a single trace may span numerous services, offering end-to-end visibility into request execution.
How do Observability Tools Work?
Observability tools help organizations understand system behavior by continuously collecting and organizing data from applications, infrastructure, and services. This data may include performance metrics, logs, and signals related to availability, latency, and resource usage, enabling consistent visibility across complex environments.
Once collected, observability tools present this information through centralized views such as dashboards and visual interfaces. These views allow teams to track application health, examine service dependencies, and understand how different components interact within the overall architecture.
Observability tools also analyze data from multiple sources to identify patterns, highlight abnormal behavior, and support issue investigation. By correlating signals across services and environments, they help teams focus on service performance and reliability objectives, turning raw telemetry into actionable insights that support faster troubleshooting and informed decision-making.
Monitoring vs. APM vs. Observability
Monitoring, Application Performance Monitoring (APM), and Observability are closely related practices used to maintain system reliability and performance, but they differ in scope and purpose.
Monitoring is primarily used to track predefined metrics and thresholds within individual systems or components. It helps teams detect known conditions, such as resource exhaustion or service downtime, and triggers alerts when these conditions are met. This approach is effective for identifying expected problems but offers limited context when systems behave in unexpected ways.
APM extends traditional monitoring by focusing on application-level performance, particularly user transactions, response times, and dependencies between services. It provides deeper visibility into how applications perform and helps diagnose performance bottlenecks, but it is typically scoped to specific applications and predefined performance indicators.
Observability takes a broader and more dynamic approach by correlating data across applications, services, and infrastructure to provide contextual insight into system behavior. Instead of relying solely on predefined conditions, observability enables teams to explore interactions across distributed systems and investigate unfamiliar issues by examining how components influence one another. This makes it especially valuable in cloud-native and microservices-based environments.
Observability Use Cases
Observability is used across modern IT environments to help teams. Here are some of its use cases:
- Real-time system visibility
Enables continuous insight into application and service health across distributed environments, supporting faster issue detection and investigation. - Cloud migration and modernization
Helps teams maintain visibility as applications move to hybrid and multi-cloud architectures, where system complexity increases. - Operational efficiency and reliability
Reduces the time required to identify and resolve issues, improving service stability and overall operational performance. - DevSecOps enablement
Provides continuous feedback across development, security, and operations, supporting the delivery of resilient and dependable applications. - AI-assisted system optimization
Some observability platforms use analytics and machine learning to identify patterns, anticipate performance issues, and support proactive optimization.
Key Terms
Anomaly Detection
The process of cleaning and validating user input to ensure that only safe data is accepted, preventing malicious inputs that could lead to vulnerabilities.
Synthetic Monitoring
A monitoring approach that uses simulated user interactions to measure application availability and performance under controlled conditions, often used alongside observability for baseline checks.
Incident Response
A structured process for identifying, investigating, and resolving system incidents to reduce service disruption and restore normal operations.