Observability What is observability?
Observability allows you to swiftly analyze, diagnose, and fix issues without direct access to a system's internal workings by measuring its outputs, such as logs, metrics, and traces. By collecting and interpreting these outputs, organizations can diagnose issues, monitor performance, and ensure reliability in complex, distributed systems. Observability goes beyond typical monitoring by enabling a detailed insight into the system's state under any condition, empowering teams to respond to unknown or unexpected actions.
Time to read: 12 minutes 02 seconds | Updated: February 9, 2026
Table of Contents
What is observability in modern IT systems?
Observability is the ability to understand a system's internal state by analyzing its outputs and enabling effective debugging. Today's IT systems are often complex and distributed, using technologies such as microservices and serverless functions. Unlike traditional monitoring, observability enables deeper exploration of how a system operates, even when issues are unexpected. It depends on rich data sources, such as metrics, logs, events, and distributed traces.
What sets observability apart is that it lets you ask new and unexpected questions about your systems. Traditional monitoring relies on static dashboards and preset alert thresholds to answer predefined questions, such as "Is CPU usage above 90%?". Observability, on the other hand, provides detailed data that helps answer new questions as they arise, such as, "Why are only users on a specific iOS version in the EMEA region experiencing slow load times after the latest deployment?".
This ability to answer new questions is crucial for addressing unexpected issues in complex systems. You can't set up an alert for an issue you've never seen before. Observability provides engineers with the detailed data needed to investigate new issues, trace their causes across multiple services, and understand their impact on the system. It assumes failures are inevitable and equips teams to quickly analyze them using data.
Modern observability connects system performance directly to business outcomes. By combining business details—like a user's shopping cart ID or subscription level—with technical data such as a slow API response, your team can directly see how technical issues impact business objectives. For example, engineers can link a database error to an increase in "failed checkout" events, allowing them to measure the financial impact of a bug and prioritize fixes based on business impact rather than technical urgency.
What are the core data signals of an observable system?
The core signals of an observable system are the telemetry types collected to fully understand its behavior. Although observability is rooted in a foundational trio, today's approaches extend further to meet the challenges of increasingly complex system architectures.
The three foundational pillars of observability are metrics, logs, and traces:
- Metrics are numeric, time-series data points that are aggregated over intervals. They are essential for tracking system health over time, offering quick insights into performance, resource usage (such as CPU or memory), and error rates. Metrics are useful for creating dashboards and triggering alerts on known conditions.
- Logs are immutable, timestamped records of discrete events. They are used to capture events with rich, detailed context, such as an error message with a full stack trace or a record of a user login. Metrics show that a problem has occurred, while logs provide the contextual details that explain why.
- Distributed traces reveal a request's end-to-end journey through a system. Traces map out the entire workflow by following a single user action as it travels across multiple microservices, databases, and APIs. This is invaluable for pinpointing latency bottlenecks and understanding dependencies in distributed architectures. However, in complex cloud‑native environments, the three pillars alone are often insufficient. Massive data volumes and short‑lived services make it challenging to manually connect different data types to identify root causes. This has led to emerging data signals that provide deeper insight, including continuous profiling and business events.
- Continuous profiling helps pinpoint resource-intensive code by constantly analyzing CPU and memory usage down to the function or line number. It explains why a service is slow or resource‑intensive, connecting trace data that shows where time is spent with the exact code responsible.
- The significance of business events lies in connecting technical performance to business outcomes. By treating high-value actions like "cart_add" or "payment_processed" as first-class telemetry, teams can directly measure the business impact (e.g., revenue lost) of technical issues, enabling data-driven prioritization.
How do you implement an observability strategy?
Observability strategies use technology, standards, and a cultural attitude to understand system behavior. Observability is focused on cultivating sustainable discipline rather than simply deploying a tool.
Modern observability pipelines link data creation to insight. Instrumentation means configuring application and infrastructure code to emit telemetry. A collection layer (like an agent) collects and sends this data to a central processing and storage backend. Data is indexed, correlated, and stored. In the last stage, engineers employ querying languages, dashboards, and alerting systems to analyze data, find trends, and fix bugs.
Modern instrumentation relies on OpenTelemetry (OTel), a Cloud Native Computing Foundation (CNCF) project and industry standard. OTel brings together vendor-neutral APIs, SDKs, and tools for metrics, logs, and traces. A key benefit is freedom from vendor lock‑in. By instrumenting services once with OTel, data can be routed to any supported backend, enabling teams to change analytic platforms without rewriting application code.
When choosing tools, organizations might choose an integrated observability platform and best-of-breed solutions. An integrated platform provides a "single pane of glass" that automatically connects traces, logs, and metrics for smooth debugging. A best‑of‑breed strategy allows teams to select the optimal tool for each function, such as logging or tracing, but it increases integration and maintenance complexity.
Finally, technology alone is insufficient. A cultural shift toward data-driven curiosity is needed for observability success. Rather than simply reacting to notifications, engineers should be empowered to ask questions and probe into 'unknown unknowns'. This fosters collaboration between development, operations, and business teams utilizing observable data and a blameless culture that views incidents as learning opportunities.
Observability vs. monitoring: What's the difference between observability vs. monitoring?
Observability and monitoring are both necessary for system reliability, although they serve different purposes. Monitoring employs established measurements and thresholds to discover known issues, while observability analyzes a system's external outputs—logs, metrics, and traces—to infer its internal state and find unknown issues. Monitoring helps respond to problems as they happen, while observability helps you understand system behavior Observability and monitoring are both necessary for system reliability, although they serve different purposes. Monitoring employs established measurements and thresholds to discover known issues, while observability analyzes a system's external outputs—logs, metrics, and traces—to infer its internal state and find unknown issues. Monitoring helps respond to problems as they happen, while observability helps you understand system behavior to prevent and fix them.
Here's a more detailed breakdown:
Monitoring:
- Focus: Tracks and displays metrics, issues warnings for predetermined situations, and provides a dynamic view of system health.
- Goal: Identify and fix issues quickly.
- Data: Mostly uses predefined metrics and log data.
- Example: Tracking memory consumption, HTTP response times, and disk I/O to pinpoint performance issues.
Observability:
- Focus: It focuses on system outputs to uncover unknown issues and comprehend complicated behaviors.
- Goal: Develop insights into system behavior for proactive problem detection and root cause investigation.
- Data: Gathers measurements, logs, and traces for a complete account of system processes.
- Example: Tracking request journey across microservices using distributed traces or analyzing logs to identify service malfunctions.
Key differences:
- Observability focuses on preventing errors before they impact users, whereas monitoring acts as a warning system.
- Monitoring addresses recognized faults with established metrics, while observability analyzes system outputs and behaviors to find unknown issues.
- Monitoring focuses on individual metrics, but observability provides a comprehensive picture of the system's internal state.
- Observability provides a comprehensive root cause investigation by studying the system's whole context, while monitoring may only indicate faults without providing sufficient context.
What are the three pillars of observability?
The three observability pillars
Metrics, logs, and traces are essential to analyzing a system's health, performance, and behavior. The combined insights from each pillar provide a complete picture of system activities. Traces follow distributed system request flow, metrics provide numerical data on system behavior and resource use, and logs document system occurrences. These data types help developers and operations teams analyze and fix faults, boosting system reliability.
Metrics: A quantitative system behavior
Metrics measure system health and behavior numerically. This aggregated data helps discover patterns, create alarm thresholds, and track resource consumption.
- Common metrics for monitoring system performance include CPU use, memory consumption, network latency, and request rates.
- Metrics can identify anomalies, such as resource use spikes, that may suggest underlying concerns.
- Metrics alone cannot identify specific issues or root causes without additional data types.
Logs: A comprehensive system event records
Logs record system events at a specified time. It gives detailed system activity data for debugging and root cause analysis.
- Logs can indicate failures, warnings, unsuccessful database requests, or authentication concerns.
- Logs help teams identify the sequence of events that led to system failures or performance issues.
- Large log volumes in dispersed systems necessitate powerful filtering and indexing techniques for useful insights.
Traces: End-to-end tracking of requests
Traces track distributed system requests and transactions. They reveal how services interact and how long actions take, making them essential for diagnosing bottlenecks and delays.
- A trace can reveal a user request's exact path between microservices, revealing latency.
- Traces are useful in microservices designs to identify performance bottlenecks and failed dependencies, as a single request can travel through numerous services.
- Implementing full instrumentation across all services might be resource-intensive for effective tracing.
What are the benefits of observability?
Observability improves system performance, reliability, user satisfaction, operational efficiency, and IT outcomes aligned with business goals. Observability allows teams to debug, optimize performance, and prevent issues from affecting users or business operations by offering extensive system behavior insights. The main benefits are detailed below:
1. Better troubleshooting and resolution
Quicker root cause analysis: Observability tools provide detailed data to help teams find issues. This reduces guesswork and speeds resolution.
Reduced MTTD and MTTR: Observability speeds up troubleshooting, letting teams focus on innovation.
Proactive issue detection: Observability tools can spot abnormalities and possible issues before they affect users, allowing teams to fix and avert outages.
Reduced alert fatigue: Observability lowers irrelevant alarms and concentrates on actionable ones, enhancing team efficiency and lowering burnout by offering context-rich insights into concerns.
2. Better system performance and dependability
Better uptime and reliability: Observability gives teams real-time access into system performance to detect and fix bottlenecks.
Performance optimization: Teams can find inefficiencies and optimize system performance by evaluating data, traces, and logs.
Faster software delivery at scale: Observability gives teams comprehensive visibility into system activity, enabling them to confidently deploy, update, and scale software with few disruptions.
3. Infrastructure, cloud, and kubernetes monitoring
Modern distributed systems like cloud platforms, on-premises infrastructure, and Kubernetes clusters require observability.
Benefit: Teams can maximize resource use, manage containerized workloads, and scale services seamlessly.
Observability tools can monitor Kubernetes pod health, detect failed deployments, and optimize cloud resource costs for efficiency.
4. A better user experience
By decreasing downtime, boosting performance, and addressing issues before they worsen, observability keeps programs stable and responsive, improving user experience.
User Satisfaction: A smoother, more dependable system increases user satisfaction and loyalty, improving customer retention and business success.
5. Business analytics
Observability connects IT operations to business outcomes by giving decision-making data.
Benefit: Teams may link technical measurements to company KPIs like revenue, user retention, and customer happiness.
Observability solutions can assess the impact of downtime on income, enabling firms to pick improvements with the highest ROI.
6. DevOps/DevSecOps Automation
Observability data optimizes CI/CD pipelines, resource scaling, and incident response workflows, streamlining automation. Reduces manual involvement and boosts efficiency.
Improved Security: Observability tools can discover anomalies, suspicious activities, and security weaknesses, helping teams prevent threats and defend against them.
7. Operation efficiency improved
Observability automates alerts, anomaly detection, and root cause investigation to streamline workflows. This lowers manual labor and lets teams focus on strategic goals, improving operational efficiency.
8. Cost effectiveness
Observability lowers operational costs by enhancing system efficiency, decreasing downtime, and optimizing resource use. By finding unused cloud resources, businesses may save money without sacrificing performance.
9. Benefits of data visibility
Data pipeline observability helps teams verify data quality, integrity, and compliance beyond system performance.
What is the future of observability—AI and observability?
Future of observability: AI and trends
AI, automation, and new computing paradigms shape observability as systems become more complex. These new developments make system monitoring and management more intelligent, automated, and adaptive. Here are its key developments.
1. AI-powered observability
AI and machine learning enable large-scale anomaly identification and prediction insights, revolutionizing observability.
- AI-powered observability technologies can spot anomalies in real-time, enabling teams to handle possible issues before they worsen.
- Predictive observability: Machine learning models provide proactive solutions to system failures, resource shortages, and performance bottlenecks, reducing downtime and improving reliability.
AI observability increases root cause analysis, reduces fatigue alert, and strengthens systems.
2. New domain observability
Observability is increasing to incorporate serverless, edge, and IoT technologies.
- Serverless and kubernetes: Observability solutions adapt to dynamic contexts like Kubernetes and serverless architecture, enabling seamless distributed system monitoring.
- IoT and edge computing: Edge computing and IoT devices make observability crucial for monitoring distributed infrastructures and maintaining data integrity across connected devices.
Modern, decentralized systems require observability, which these advances provide.
3. Automation and observability-as-code integration
The trend is to combine observability with AIOps and automation. Observability-as-code methods simplify programmatic observability configuration definition and management, harmonizing with DevOps workflows and enhancing scalability.
How HPE and OpsRamp are transforming Observability for Hybrid Cloud and AI?
HPE and OpsRamp are redefining observability with their hybrid cloud management and AI-driven operations expertise. Their alliance addresses the challenges of managing modern IT environments, which are increasingly scattered across on-premises, cloud, and edge infrastructures. HPE and OpsRamp help enterprises build durable, scalable, and efficient systems by integrating robust observability with AI and automation.
Improved hybrid cloud observability
Management of distributed workloads, interoperability, and visibility across heterogeneous infrastructures are unique to hybrid cloud settings. The HPE and OpsRamp solutions address these issues:
- Their unified monitoring platform provides visibility into on-premises, cloud, and edge systems, allowing enterprises to monitor hybrid cloud infrastructures from a single pane.
- OpsRamp's technology provides extensive insights into infrastructure health, resource use, and performance in hybrid settings.
Observability by AI
HPE and OpsRamp are using advanced AI to improve observability:
- Proactive Anomaly Detection: AI allows proactive anomaly detection in hybrid cloud systems, preventing possible issues from affecting operations.
- Predictive Analytics: Machine learning models estimate resource needs and system behavior, enabling proactive scaling and optimization.
- Faster Issue Resolution: AI-powered root cause investigation and automated remediation lower MTTR, enabling faster incident recovery.
Integrating automation with AIOps
The alliance emphasizes automating IT operations using observability and AIOps:
- Event Correlation: OpsRamp's technology intelligently links observability data with incident management workflows, minimizing noise and boosting decision-making.
- Automated Remediation: AI-driven tools enable IT professionals to focus on strategic projects by automating corrective activities.
Support for edge computing and IoT
HPE and OpsRamp provide visibility and management over massively distributed edge computing and IoT devices. This is essential for enterprises managing data and workloads across linked devices and remote infrastructures.
FAQs
What is a simple example of observability in action?
A user reports a slow checkout. With a platform like HPE OpsRamp, engineers can trace a single user request across all services. OpsRamp's correlated data pinpoints the true bottleneck—a slow database query—rather than just flagging a generic CPU alert. Providing contextual and actionable answers enables rapid root‑cause resolution. This elevates observability beyond simple monitoring toward intelligent problem solving.
Is observability only for microservices and Kubernetes?
No. Although observability is crucial for complex systems, it can be used in any environment. HPE OpsRamp, for example, is built for hybrid IT environments and provides detailed visibility into both modern cloud-native apps and traditional monolithic systems. It unifies all observability data, enabling teams to tackle new challenges and understand how everything connects, regardless of the setup.
What is the difference between observability and application performance management (APM)?
APM represents just one aspect of observability, traditionally focused on measuring application response times. Modern observability platforms like HPE OpsRamp let you study 'unknown unknowns'. OpsRamp connects application data with infrastructure changes to uncover unexpected root causes, moving beyond static dashboards to true investigation.
How do I start implementing observability in my organization?
Start with a crucial service and deploy HPE OpsRamp. Start collecting metrics, logs, events, and traces using its discovery and instrumentation. OpsRamp's AIOps engine automatically correlates the data, delivering quick and relevant insights. This enables teams to demonstrate value quickly and scale observability practices enterprise‑wide through a single solution.
Is the HPE OpsRamp software suite a complete observability tool?
HPE OpsRamp is an AI-powered platform that gives you full visibility into your hybrid IT environments. It unifies observability for infrastructure, cloud services, and applications by analyzing metrics, logs, traces, and events. The event management engine correlates data to provide smart root‑cause analysis and service‑level insights, positioning it as a strong choice for enterprises.
Can observability help predict system failures before they happen?
Yes, HPE OpsRamp uses machine learning to analyze anomalies and forecast issues such as latency spikes or unusual error patterns. This enables preemptive issue resolution before failures affect users, boosting system stability and uptime.
How does data correlation work in observability?
HPE OpsRamp automates data correlation, using context such as request IDs to link metrics, logs, and traces. OpsRamp reveals the specific traces and logs from a metric spike. This unifies siloed data into an actionable narrative, expediting root cause investigation.