Why Is Observability Important?
Modern applications are increasingly distributed.
A single digital experience may rely on dozens or hundreds of services, APIs, databases, containers and cloud resources. A slowdown experienced by a user may originate in application code, a Kubernetes workload, a database query, an external API or an infrastructure dependency.
Traditional monitoring can tell an engineering team that response time has increased. Observability helps the team investigate why response time increased.
Understanding the distinction between observability and monitoring is especially important as architectures become more distributed and dynamic.
Containers appear and disappear. Infrastructure scales automatically. Software changes are deployed continuously. Dependencies may span multiple clouds and third-party services. Observability helps teams investigate these systems without having to predict every possible failure in advance.
Observability Helps Answer Questions Such As:
- Why did application latency increase after a deployment?
- Which service is causing a transaction to fail?
- Why are only some users experiencing errors?
- Which dependency is responsible for a performance bottleneck?
- What changed immediately before an incident?
- How did a failure propagate across services?
- Is infrastructure performance affecting application reliability?
- Are we collecting the right telemetry to troubleshoot this issue?
The goal is not simply more data. The goal is enough relevant, correlated context to explain system behavior and take action.
How Does Observability Work?
Observability works by instrumenting systems to produce telemetry, collecting that telemetry and correlating it with information about applications, services, infrastructure and users.
A typical observability workflow includes six stages.
1. Instrument the Environment
Applications, services and infrastructure must produce meaningful telemetry. Instrumentation may be built directly into applications or implemented using an open framework such as OpenTelemetry, which provides vendor-neutral APIs, SDKs and tools for generating and collecting observability data.
2. Collect Telemetry
Telemetry is gathered from applications, cloud services, infrastructure, containers, databases, APIs and other components.
This data commonly includes metrics, logs and traces, along with events, profiles and metadata. Understanding the differences between logs, metrics and traces helps teams determine which signals are most useful for different types of investigations.
3. Process and Route the Data
A telemetry pipeline collects, processes and routes observability data before it reaches storage and analytics systems. Organizations may filter, aggregate, enrich, transform, sample or route telemetry to improve data quality and control observability costs.
4. Correlate Signals and Context
Individual signals provide useful information, but correlation is what makes observability powerful.
A latency metric may indicate that an application is slowing down. A trace can identify the service contributing to that latency. Logs may then reveal the error or configuration change associated with the service. Metadata such as deployment version, cloud region, customer tier or Kubernetes namespace can provide additional context.
5. Analyze System Behavior
Observability platforms help engineering teams query, visualize and analyze telemetry. Teams can use dashboards and alerts for known conditions while exploring correlated telemetry when unexpected issues arise.
6. Diagnose and Remediate
The final goal is action. Once teams understand where a problem originated and what caused it, they can remediate the issue and use subsequent telemetry to confirm that system behavior has returned to normal.
What Are the Three Pillars of Observability?
The three traditional pillars of observability are metrics, logs and traces.
They are better understood as complementary signals. Simply collecting all three does not automatically make a system observable; the data must be sufficiently detailed, contextual and connected to support investigation.
For a deeper comparison, see Logs vs. Metrics vs. Traces.
Metrics
Metrics are numerical measurements collected over time.
Common examples include:
- Request rate
- Error rate
- Response time
- CPU usage
- Memory consumption
- Network throughput
- Queue depth
- Database latency
Metrics are efficient for detecting trends and identifying when system behavior changes.
Logs
Logs are timestamped records of events generated by applications, operating systems, infrastructure and services. A log might record an application exception, authentication failure, configuration change or database error. Logs provide detailed evidence that can help explain what happened before and during an incident.
Traces
Traces follow an individual request as it moves through an application. In distributed architectures, distributed tracing shows which services participated in a request, how long each operation took and where errors or latency occurred.
Beyond Metrics, Logs and Traces
Modern observability increasingly depends on context beyond the traditional three signals.
This may include:
- Deployment events
- Infrastructure topology
- Service dependencies
- Continuous profiling
- User-experience data
- Cloud metadata
- Kubernetes metadata
- Code-level information
- Business transaction context
- AI application telemetry
These additional signals help teams connect technical behavior with the application, user and business outcomes affected by it.
What Is an Example of Observability?
Consider an online application whose checkout page suddenly becomes slow. A monitoring system detects that response time has crossed a predefined threshold and triggers an alert. Observability helps engineers investigate what happens next.
- Metrics show that checkout latency increased immediately after a new application release.
- Traces show that requests are spending most of their time waiting for the inventory service.
- Logs from that service reveal repeated database query timeouts.
- Deployment metadata shows that the inventory service was updated shortly before latency increased.
Together, these signals give the engineering team a testable explanation: a recent inventory-service change appears to have introduced slow database queries.
The team can investigate or roll back the change and then use telemetry to determine whether checkout performance returns to normal. That is the practical difference between simply detecting a problem and having enough observability to understand it.
Observability vs. Monitoring: What Is the Difference?
Monitoring and observability are complementary practices, but they answer different types of questions. Monitoring focuses primarily on known conditions. Observability provides the context necessary to investigate both known and unknown conditions.
For example, monitoring may alert a team when API latency exceeds 500 milliseconds. Observability can help determine whether the latency originates in an application service, database, network dependency or downstream API.
See Observability vs. Monitoring: Key Differences for a deeper comparison.
What Is the Difference Between Telemetry and Observability?
Telemetry is the data a system produces. Observability is the ability to use that data to understand the system. Metrics, logs and traces are therefore not observability themselves. They are telemetry signals that help make a system observable.
Collecting enormous quantities of telemetry without sufficient context, correlation or query capabilities may increase cost without significantly improving troubleshooting. A well-designed telemetry pipeline can help organizations manage, filter and route telemetry before it reaches downstream observability platforms.
What Are the Benefits of Observability?
Observability can improve both technical performance and engineering efficiency.
- Faster Root-Cause Analysis: Correlating telemetry across services helps teams narrow an investigation from a general symptom to the components most likely responsible.
- Reduced Mean Time to Resolution: When engineers can identify affected dependencies and understand system behavior more quickly, they can spend less time manually searching across disconnected tools and datasets.
- Improved Application Reliability: Observability helps teams identify recurring failure patterns, performance degradation and emerging reliability problems. Reliability teams can pair observability with service level objectives (SLOs) to define measurable service reliability targets.
- Better User Experience: Connecting backend system behavior with frontend or user-experience telemetry helps teams understand how technical issues affect customers.
- Greater Visibility Across Distributed Systems: Observability provides a way to investigate applications whose behavior spans microservices, APIs, containers and cloud infrastructure.
- More Effective Capacity and Performance Planning: Historical telemetry helps teams identify trends in resource utilization, traffic and system performance.
- Better Engineering Productivity: Developers and SREs can spend less time trying to reproduce or manually locate problems and more time resolving them.
- More Informed Automation: Correlated observability data can support anomaly detection, automated investigations, AIOps and remediation workflows.
What Are Common Observability Use Cases?
Organizations use observability across many operational workflows.
Application Performance Troubleshooting
Teams analyze latency, errors, throughput and dependencies to identify application performance problems.
Incident Investigation
Observability helps incident responders reconstruct what changed, determine which services were affected and isolate likely root causes.
Microservices and Distributed Systems
Service maps and distributed tracing help engineers understand interactions across large numbers of interconnected services.
Infrastructure Monitoring and Troubleshooting
Infrastructure telemetry helps teams correlate CPU, memory, storage, networking and cloud-resource behavior with application performance.
Kubernetes and Container Observability
Dynamic container environments generate constantly changing workloads and infrastructure relationships.
Kubernetes observability helps teams connect application behavior with Kubernetes clusters, nodes, pods, containers and associated infrastructure.
Database and API Performance
Telemetry can reveal slow database operations, failed queries, API errors and external dependency latency.
Deployment and Change Analysis
Teams can correlate performance changes with software releases, infrastructure updates, configuration changes and feature flags.
Service Reliability Management
Observability provides the telemetry needed to measure service performance against reliability targets. A service level objective (SLO) defines a target level of reliability for a service. Understanding SLAs, SLOs and SLIs helps teams connect operational telemetry with measurable reliability goals and customer expectations.
Observability in Cloud-Native Environments
Observability becomes particularly important in cloud-native systems because infrastructure and application relationships change continuously. Containers may exist for only minutes. Services scale automatically. Requests cross multiple APIs and microservices. Workloads can move between nodes, clusters or cloud regions.
Cloud native observability connects telemetry with dynamic infrastructure context so engineers can understand these changing environments.
Effective cloud native observability commonly includes visibility into:
- Applications and services
- Containers and Kubernetes
- Cloud infrastructure
- Service dependencies
- APIs
- Databases
- Network behavior
- Deployments
- User experience
For Kubernetes-specific environments, Kubernetes observability provides visibility into workloads, clusters, nodes and containers while preserving application context.
The objective is end-to-end understanding rather than separate views of each technology layer.
What Is High Cardinality in Observability?
Cardinality describes the number of unique values within a data attribute.
For example, a status field containing success and failure has low cardinality. An attribute containing millions of unique user IDs has high cardinality.
High-cardinality telemetry can provide valuable investigative detail because engineers can filter telemetry by attributes such as customer, service, container or request. However, uncontrolled cardinality can increase storage requirements, query complexity and observability costs.
Organizations therefore need to preserve useful investigative dimensions while controlling unnecessary or low-value telemetry.
What Is AI Observability?
AI applications introduce additional forms of behavior that traditional infrastructure telemetry does not fully describe. AI observability extends observability to AI applications, models, prompts, retrieval systems, agents, tools and the infrastructure supporting them.
In addition to conventional latency and infrastructure signals, teams may need to understand:
- Model response latency
- Token consumption
- Model and inference cost
- Prompt and response behavior
- Retrieval performance
- Tool calls
- Agent workflows
- Task completion
- Model versions
- Application errors
- AI workload infrastructure
Organizations can use AI observability metrics to evaluate the reliability, performance, quality and cost of AI applications.
At the model layer, observability in AI models provides additional insight into model behavior, performance and operational changes.
What Are Common Observability Challenges?
Observability can become difficult to manage as systems and telemetry volumes grow.
Telemetry Volume and Cost
Modern environments can generate enormous quantities of metrics, logs and traces. Collecting everything indefinitely is rarely economical. Organizations need mechanisms such as a telemetry pipeline to prioritize useful telemetry, manage retention and reduce low-value data.
High Cardinality
High cardinality provides valuable debugging context but can increase query and storage costs when poorly controlled.
Tool Sprawl
Separate monitoring, logging, tracing and infrastructure tools can fragment investigations. Engineers may need to switch between systems manually to reconstruct an incident.
Missing Context
Telemetry without service ownership, topology, deployment, environment and other metadata can be difficult to interpret.
Inconsistent Instrumentation
Different teams may instrument services differently, producing inconsistent names, attributes and data structures. OpenTelemetry can help organizations standardize instrumentation and telemetry collection.
Alert Fatigue
Too many low-quality alerts create operational noise. Observability strategies should prioritize actionable alerts while preserving richer telemetry for investigation.
Data Governance
Telemetry can contain sensitive or regulated information. Organizations should define controls for collection, routing, storage, retention and access.
How To Implement Observability
Building observability is an ongoing engineering practice rather than simply deploying a tool.
A practical approach includes the following steps.
1. Start With Critical Services and User Journeys
- Identify the applications, services and workflows whose reliability matters most.
2. Define What Good Performance Looks Like
- Establish measurable indicators for availability, latency, errors and user experience.
- Where appropriate, define a service level objective for critical services.
3. Instrument Applications and Infrastructure
- Generate metrics, logs and traces with sufficient metadata to investigate system behavior.
- Use OpenTelemetry or another standardized instrumentation approach where appropriate.
4. Establish Consistent Telemetry Conventions
- Standardize service names, attributes, environments and ownership information so telemetry can be correlated reliably.
5. Correlate Across the Stack
- Connect application, infrastructure, database, network, deployment and user-experience data rather than analyzing each source independently.
6. Preserve Useful Context
- Capture dimensions engineers are likely to need during an investigation without allowing uncontrolled telemetry growth.
- Understanding high cardinality data is particularly important when designing telemetry dimensions.
7. Test Your Observability
- Do not wait for a major outage. Ask engineers whether they can answer questions such as:
- Which users are affected?
- Which service caused this latency?
- What changed?
- Which dependency is failing?
- When did the behavior begin?
- Can we confirm the problem is resolved?
If the telemetry cannot answer these questions, instrumentation or context may be missing.
8. Continuously Optimize
- Review which telemetry is actually used, which alerts are actionable and which data adds cost without improving investigations.
Observability should evolve with the architecture it supports.
What Should You Look for in an Observability Platform?
An observability platform should help teams move efficiently from detection to investigation and resolution. Important capabilities include:
- Unified telemetry: Ability to analyze metrics, logs, traces and related contextual data together.
- Open instrumentation: Support for standards such as OpenTelemetry.
- Cross-system correlation: Ability to connect services, infrastructure, dependencies, deployments and user experience.
- High-cardinality analysis: Support for detailed dimensional investigation at scale.
- Distributed tracing: End-to-end visibility into requests across services.
- Flexible querying: Ability to investigate questions that were not defined in advance.
- Scalability: Performance across large telemetry volumes and distributed environments.
- Telemetry cost management: Controls for filtering, aggregation, sampling, retention and data optimization.
- SLO support: Capabilities for measuring service reliability against defined objectives.
- AI-assisted analysis: Tools that help identify anomalies, correlate signals and accelerate root-cause investigation.
- Integrations: Compatibility with cloud platforms, Kubernetes, databases, development tools and enterprise systems.
- Governance: Controls for telemetry access, sensitive data and retention.
The best platform is not necessarily the one that collects the most data. It is the one that helps teams derive useful answers from the appropriate data with enough context to act.
Observability and Site Reliability Engineering
Observability plays an important role in site reliability engineering because SRE teams must measure service behavior, investigate reliability issues and determine whether systems are meeting defined objectives.
The relationship between SLAs, SLOs and SLIs provides a framework for connecting observed system behavior with measurable reliability expectations. An SLI measures service behavior, an SLO defines the desired target and an SLA establishes an external commitment.
Observability provides the telemetry teams need to evaluate those measurements and investigate why reliability targets are missed.
Observability and Security
Observability and security both rely on telemetry, but they focus on different questions.
- Observability primarily helps engineering teams understand application health, performance, availability and reliability.
- Security teams focus on threats, suspicious behavior, vulnerabilities and policy violations.
The underlying data can overlap. Application logs, cloud events, API activity and infrastructure telemetry may provide value to both teams. Shared telemetry can therefore improve collaboration between operations and security, provided organizations maintain appropriate access, governance and analytical workflows.
How Does Observability Support AIOps?
Observability provides the operational data and context required for AIOps and AI-driven operations.
- Machine learning and AI systems can analyze telemetry to identify unusual behavior, correlate incidents and prioritize potential causes.
- More advanced systems can use application topology, historical incident data and real-time telemetry to support root-cause analysis and remediation workflows.
AI does not eliminate the need for high-quality observability data. It increases the importance of accurate instrumentation, useful context and consistent telemetry.
Observability With Cortex XCOR
Cortex XCOR is Palo Alto Networks' AI-driven observability platform for understanding and operating applications and infrastructure across complex environments. It brings together observability data and operational context to help engineering teams investigate issues, improve reliability and optimize telemetry at scale.
Explore Cortex XCOR to learn more about AI-driven observability.
Observability FAQs
