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[![Identity Security logo](https://www.paloaltonetworks.com/content/dam/pan/en_US/images/logos/nav/idira-logo-nav.svg)](https://www.paloaltonetworks.com/idira?ts=markdown) [Human Identities](https://www.paloaltonetworks.com/idira/human?ts=markdown) * [Privileged Access Management](https://www.paloaltonetworks.com/idira/human/privileged-access-management?ts=markdown) * [Identity and Access Management](https://www.paloaltonetworks.com/idira/human/identity-and-access-management?ts=markdown) * [Endpoint Privilege Manager](https://www.paloaltonetworks.com/idira/human/endpoint-privilege-manager?ts=markdown) * [Identity Governance](https://www.paloaltonetworks.com/idira/human/identity-governance?ts=markdown) * [Workforce Password Management](https://www.paloaltonetworks.com/idira/human/workforce-password-management?ts=markdown) * [Vendor Privileged Access](https://www.paloaltonetworks.com/idira/human/vendor-privileged-access?ts=markdown) [Machine 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Core Signals, Benefits, and Use Cases](https://www.paloaltonetworks.com/cyberpedia/what-is-observability?ts=markdown) * [Why Observability Matters](https://www.paloaltonetworks.com/cyberpedia/what-is-observability#matters?ts=markdown) * [How Observability Works](https://www.paloaltonetworks.com/cyberpedia/what-is-observability#works?ts=markdown) * [The Core Signals of Observability](https://www.paloaltonetworks.com/cyberpedia/what-is-observability#core?ts=markdown) * [Benefits of Observability](https://www.paloaltonetworks.com/cyberpedia/what-is-observability#benefits?ts=markdown) * [Common Observability Use Cases](https://www.paloaltonetworks.com/cyberpedia/what-is-observability#usecase?ts=markdown) * [Observability vs. Security](https://www.paloaltonetworks.com/cyberpedia/what-is-observability#security?ts=markdown) * [Common Observability Challenges](https://www.paloaltonetworks.com/cyberpedia/what-is-observability#challenges?ts=markdown) * [What Makes a System Observable?](https://www.paloaltonetworks.com/cyberpedia/what-is-observability#observable?ts=markdown) * [Observability in Cloud Native Environments](https://www.paloaltonetworks.com/cyberpedia/what-is-observability#environments?ts=markdown) * [Observability FAQs](https://www.paloaltonetworks.com/cyberpedia/what-is-observability#page-anchor?ts=markdown) * [What Is a Telemetry Pipeline?](https://www.paloaltonetworks.com/cyberpedia/telemetry-pipeline?ts=markdown) * [Why are Telemetry Pipelines Important?](https://www.paloaltonetworks.com/cyberpedia/telemetry-pipeline#why?ts=markdown) * [How a Telemetry Pipeline Works](https://www.paloaltonetworks.com/cyberpedia/telemetry-pipeline#how?ts=markdown) * [What Data Moves Through a Telemetry Pipeline?](https://www.paloaltonetworks.com/cyberpedia/telemetry-pipeline#what?ts=markdown) * [Core Telemetry Pipeline Functions](https://www.paloaltonetworks.com/cyberpedia/telemetry-pipeline#core?ts=markdown) * [Telemetry Pipeline Architecture](https://www.paloaltonetworks.com/cyberpedia/telemetry-pipeline#pipeline?ts=markdown) * [Telemetry Pipelines and OpenTelemetry](https://www.paloaltonetworks.com/cyberpedia/telemetry-pipeline#telemetry?ts=markdown) * [Telemetry Pipeline vs. Observability Pipeline](https://www.paloaltonetworks.com/cyberpedia/telemetry-pipeline#vs?ts=markdown) * [Telemetry Pipeline Benefits](https://www.paloaltonetworks.com/cyberpedia/telemetry-pipeline#benefits?ts=markdown) * [Telemetry Pipeline Risks](https://www.paloaltonetworks.com/cyberpedia/telemetry-pipeline#risks?ts=markdown) * [Telemetry Pipeline Best Practices](https://www.paloaltonetworks.com/cyberpedia/telemetry-pipeline#best?ts=markdown) * [Telemetry Pipeline FAQs](https://www.paloaltonetworks.com/cyberpedia/telemetry-pipeline#faqs?ts=markdown) * [What Is AI Observability? Benefits and How It Works](https://www.paloaltonetworks.com/cyberpedia/what-is-ai-observability?ts=markdown) * [Why AI Systems Require New Observability](https://www.paloaltonetworks.com/cyberpedia/what-is-ai-observability#why?ts=markdown) * [AI Observability vs. AI Monitoring](https://www.paloaltonetworks.com/cyberpedia/what-is-ai-observability#ai?ts=markdown) * [Core AI Observability Signals](https://www.paloaltonetworks.com/cyberpedia/what-is-ai-observability#core?ts=markdown) * [Tracing AI Applications and Agents](https://www.paloaltonetworks.com/cyberpedia/what-is-ai-observability#tracing?ts=markdown) * [AI Observability, AI Security and AI Governance](https://www.paloaltonetworks.com/cyberpedia/what-is-ai-observability#governance?ts=markdown) * [AI for Observability vs. Observability for AI](https://www.paloaltonetworks.com/cyberpedia/what-is-ai-observability#vs?ts=markdown) * [AI Observability Best Practices](https://www.paloaltonetworks.com/cyberpedia/what-is-ai-observability#best?ts=markdown) * [Common AI Observability Challenges](https://www.paloaltonetworks.com/cyberpedia/what-is-ai-observability#common?ts=markdown) * [AI Observability FAQs](https://www.paloaltonetworks.com/cyberpedia/what-is-ai-observability#faqs?ts=markdown) * Logs vs. Metrics vs. Traces: Key Differences * [Logs vs. Metrics vs. Traces at a Glance](https://www.paloaltonetworks.com/cyberpedia/logs-vs-metrics-vs-traces#logs?ts=markdown) * [What Are Logs?](https://www.paloaltonetworks.com/cyberpedia/logs-vs-metrics-vs-traces#what?ts=markdown) * [What Are Metrics?](https://www.paloaltonetworks.com/cyberpedia/logs-vs-metrics-vs-traces#metrics?ts=markdown) * [What Are Traces?](https://www.paloaltonetworks.com/cyberpedia/logs-vs-metrics-vs-traces#traces?ts=markdown) * [How Do Logs, Metrics and Traces Work Together?](https://www.paloaltonetworks.com/cyberpedia/logs-vs-metrics-vs-traces#how?ts=markdown) * [Logs vs. Metrics vs. Traces: Which Should You Use?](https://www.paloaltonetworks.com/cyberpedia/logs-vs-metrics-vs-traces#use?ts=markdown) * [What Role Does OpenTelemetry Play?](https://www.paloaltonetworks.com/cyberpedia/logs-vs-metrics-vs-traces#role?ts=markdown) * [Benefits of Correlating Logs, Metrics and Traces](https://www.paloaltonetworks.com/cyberpedia/logs-vs-metrics-vs-traces#benefits?ts=markdown) * [Best Practices for Managing Logs, Metrics and Traces](https://www.paloaltonetworks.com/cyberpedia/logs-vs-metrics-vs-traces#best?ts=markdown) * [Logs vs. Metrics vs. Traces: Key Differences FAQs](https://www.paloaltonetworks.com/cyberpedia/logs-vs-metrics-vs-traces#faqs?ts=markdown) * [What Is Cloud Native Observability?](https://www.paloaltonetworks.com/cyberpedia/what-is-cloud-native-observability?ts=markdown) * [Why Cloud Native Environments Require Observability](https://www.paloaltonetworks.com/cyberpedia/what-is-cloud-native-observability#why?ts=markdown) * [How Cloud Native Observability Works](https://www.paloaltonetworks.com/cyberpedia/what-is-cloud-native-observability#native?ts=markdown) * [Cloud Native Observability and Kubernetes](https://www.paloaltonetworks.com/cyberpedia/what-is-cloud-native-observability#kubernetes?ts=markdown) * [Benefits of Cloud Native Observability](https://www.paloaltonetworks.com/cyberpedia/what-is-cloud-native-observability#benefits?ts=markdown) * [Cloud Native Observability Best Practices](https://www.paloaltonetworks.com/cyberpedia/what-is-cloud-native-observability#best?ts=markdown) * [How Palo Alto Networks Approaches Cloud Native Observability](https://www.paloaltonetworks.com/cyberpedia/what-is-cloud-native-observability#approaches?ts=markdown) * [Cloud Native Observability FAQs](https://www.paloaltonetworks.com/cyberpedia/what-is-cloud-native-observability#faqs?ts=markdown) * [What Is Distributed Tracing?](https://www.paloaltonetworks.com/cyberpedia/what-is-distributed-tracing?ts=markdown) * [Why Distributed Tracing Matters](https://www.paloaltonetworks.com/cyberpedia/what-is-distributed-tracing#why?ts=markdown) * [How Distributed Tracing Works](https://www.paloaltonetworks.com/cyberpedia/what-is-distributed-tracing#how?ts=markdown) * [Core Components of a Trace](https://www.paloaltonetworks.com/cyberpedia/what-is-distributed-tracing#core?ts=markdown) * [Distributed Tracing with OpenTelemetry](https://www.paloaltonetworks.com/cyberpedia/what-is-distributed-tracing#distributed?ts=markdown) * [Sampling Strategies](https://www.paloaltonetworks.com/cyberpedia/what-is-distributed-tracing#sampling?ts=markdown) * [Distributed Tracing Use Cases](https://www.paloaltonetworks.com/cyberpedia/what-is-distributed-tracing#tracing?ts=markdown) * [Distributed Tracing Best Practices](https://www.paloaltonetworks.com/cyberpedia/what-is-distributed-tracing#best?ts=markdown) * [Distributed Tracing FAQs](https://www.paloaltonetworks.com/cyberpedia/what-is-distributed-tracing#faqs?ts=markdown) * [What Are SRE Fundamentals: SLA vs SLO vs SLI?](https://www.paloaltonetworks.com/cyberpedia/sre-fundamentals-sla-vs-slo-vs-sli?ts=markdown) * [SRE Fundamentals Explained](https://www.paloaltonetworks.com/cyberpedia/sre-fundamentals-sla-vs-slo-vs-sli#sre-fundamentals?ts=markdown) * [SLA vs. SLO vs. SLI: A Comparative Framework](https://www.paloaltonetworks.com/cyberpedia/sre-fundamentals-sla-vs-slo-vs-sli#sla-vs-slo?ts=markdown) * [Breaking Down the Components: SLI, SLO, and SLA](https://www.paloaltonetworks.com/cyberpedia/sre-fundamentals-sla-vs-slo-vs-sli#components?ts=markdown) * [The Strategic Role of Error Budgets in SRE](https://www.paloaltonetworks.com/cyberpedia/sre-fundamentals-sla-vs-slo-vs-sli#strategic-role?ts=markdown) * [Best Practices for Implementing SRE Metrics](https://www.paloaltonetworks.com/cyberpedia/sre-fundamentals-sla-vs-slo-vs-sli#best-practices?ts=markdown) * [SRE Fundamentals FAQs](https://www.paloaltonetworks.com/cyberpedia/sre-fundamentals-sla-vs-slo-vs-sli#faqs?ts=markdown) * [Observability vs. Monitoring: Key Differences](https://www.paloaltonetworks.com/cyberpedia/observability-vs-monitoring-key-differences?ts=markdown) * [Observability vs. Monitoring Explained](https://www.paloaltonetworks.com/cyberpedia/observability-vs-monitoring-key-differences#explained?ts=markdown) * [Observability vs. Monitoring: Key Differences](https://www.paloaltonetworks.com/cyberpedia/observability-vs-monitoring-key-differences#differences?ts=markdown) * [Why Monitoring Alone Is No Longer Enough](https://www.paloaltonetworks.com/cyberpedia/observability-vs-monitoring-key-differences#why?ts=markdown) * [The Role of Telemetry in Observability](https://www.paloaltonetworks.com/cyberpedia/observability-vs-monitoring-key-differences#role?ts=markdown) * [Observability in Cloud Native and Kubernetes Environments](https://www.paloaltonetworks.com/cyberpedia/observability-vs-monitoring-key-differences#environments?ts=markdown) * [Observability and Security](https://www.paloaltonetworks.com/cyberpedia/observability-vs-monitoring-key-differences#security?ts=markdown) * [Observability for AI Systems](https://www.paloaltonetworks.com/cyberpedia/observability-vs-monitoring-key-differences#systems?ts=markdown) * [When to Use Monitoring](https://www.paloaltonetworks.com/cyberpedia/observability-vs-monitoring-key-differences#when?ts=markdown) * [When to Use Observability](https://www.paloaltonetworks.com/cyberpedia/observability-vs-monitoring-key-differences#use?ts=markdown) * [How Observability and Monitoring Work Together](https://www.paloaltonetworks.com/cyberpedia/observability-vs-monitoring-key-differences#how?ts=markdown) * [Benefits of Observability](https://www.paloaltonetworks.com/cyberpedia/observability-vs-monitoring-key-differences#benefits?ts=markdown) * [Challenges of Observability](https://www.paloaltonetworks.com/cyberpedia/observability-vs-monitoring-key-differences#challenges?ts=markdown) * [How to Build an Observability Strategy](https://www.paloaltonetworks.com/cyberpedia/observability-vs-monitoring-key-differences#strategy?ts=markdown) * [Observability vs. Monitoring FAQ](https://www.paloaltonetworks.com/cyberpedia/observability-vs-monitoring-key-differences#faqs?ts=markdown) * [What Is OpenTelemetry (OTel)?](https://www.paloaltonetworks.com/cyberpedia/what-is-open-telemetry?ts=markdown) * [OpenTelemetry Explained](https://www.paloaltonetworks.com/cyberpedia/what-is-open-telemetry#openTelemetry-explained?ts=markdown) * [The Three Pillars of OTel Signals](https://www.paloaltonetworks.com/cyberpedia/what-is-open-telemetry#the-three-pillars-of-OTel-signals?ts=markdown) * [Strategic Benefits and Advantages](https://www.paloaltonetworks.com/cyberpedia/what-is-open-telemetry#strategic-benefits-and-advantages?ts=markdown) * [Implementation Best Practices](https://www.paloaltonetworks.com/cyberpedia/what-is-open-telemetry#implementation-best-practices?ts=markdown) * [OpenTelemetry FAQs](https://www.paloaltonetworks.com/cyberpedia/what-is-open-telemetry#OpenTelemetry-faq?ts=markdown) * [What Is High Cardinality in Observability?](https://www.paloaltonetworks.com/cyberpedia/what-is-high-cardinality?ts=markdown) * [High Cardinality Explained](https://www.paloaltonetworks.com/cyberpedia/what-is-high-cardinality#explained?ts=markdown) * [Why High Cardinality Matters in Observability](https://www.paloaltonetworks.com/cyberpedia/what-is-high-cardinality#observability?ts=markdown) * [Cardinality vs. Dimensionality](https://www.paloaltonetworks.com/cyberpedia/what-is-high-cardinality#cardinality?ts=markdown) * [How High Cardinality Happens](https://www.paloaltonetworks.com/cyberpedia/what-is-high-cardinality#happens?ts=markdown) * [The Impact of High Cardinality on Observability Systems](https://www.paloaltonetworks.com/cyberpedia/what-is-high-cardinality#impact?ts=markdown) * [Example: How Cardinality Multiplies](https://www.paloaltonetworks.com/cyberpedia/what-is-high-cardinality#multiplies?ts=markdown) * [How to Reduce High Cardinality](https://www.paloaltonetworks.com/cyberpedia/what-is-high-cardinality#reduce?ts=markdown) * [Metrics vs. Logs vs. Traces for High-Cardinality Data](https://www.paloaltonetworks.com/cyberpedia/what-is-high-cardinality#metrics?ts=markdown) * [Best Practices for Managing High Cardinality](https://www.paloaltonetworks.com/cyberpedia/what-is-high-cardinality#practices?ts=markdown) * [Why High Cardinality Is a Governance Problem](https://www.paloaltonetworks.com/cyberpedia/what-is-high-cardinality#governance?ts=markdown) * [FAQs](https://www.paloaltonetworks.com/cyberpedia/what-is-high-cardinality#faq?ts=markdown) # What Is the Difference Between Logs, Metrics and Traces? 4 min. read Table of Contents * * [Logs vs. Metrics vs. Traces at a Glance](https://www.paloaltonetworks.com/cyberpedia/logs-vs-metrics-vs-traces#logs?ts=markdown) * [What Are Logs?](https://www.paloaltonetworks.com/cyberpedia/logs-vs-metrics-vs-traces#what?ts=markdown) * [What Are Metrics?](https://www.paloaltonetworks.com/cyberpedia/logs-vs-metrics-vs-traces#metrics?ts=markdown) * [What Are Traces?](https://www.paloaltonetworks.com/cyberpedia/logs-vs-metrics-vs-traces#traces?ts=markdown) * [How Do Logs, Metrics and Traces Work Together?](https://www.paloaltonetworks.com/cyberpedia/logs-vs-metrics-vs-traces#how?ts=markdown) * [Logs vs. Metrics vs. Traces: Which Should You Use?](https://www.paloaltonetworks.com/cyberpedia/logs-vs-metrics-vs-traces#use?ts=markdown) * [What Role Does OpenTelemetry Play?](https://www.paloaltonetworks.com/cyberpedia/logs-vs-metrics-vs-traces#role?ts=markdown) * [Benefits of Correlating Logs, Metrics and Traces](https://www.paloaltonetworks.com/cyberpedia/logs-vs-metrics-vs-traces#benefits?ts=markdown) * [Best Practices for Managing Logs, Metrics and Traces](https://www.paloaltonetworks.com/cyberpedia/logs-vs-metrics-vs-traces#best?ts=markdown) * [Logs vs. Metrics vs. Traces: Key Differences FAQs](https://www.paloaltonetworks.com/cyberpedia/logs-vs-metrics-vs-traces#faqs?ts=markdown) 1. Logs vs. Metrics vs. Traces at a Glance * * [Logs vs. Metrics vs. Traces at a Glance](https://www.paloaltonetworks.com/cyberpedia/logs-vs-metrics-vs-traces#logs?ts=markdown) * [What Are Logs?](https://www.paloaltonetworks.com/cyberpedia/logs-vs-metrics-vs-traces#what?ts=markdown) * [What Are Metrics?](https://www.paloaltonetworks.com/cyberpedia/logs-vs-metrics-vs-traces#metrics?ts=markdown) * [What Are Traces?](https://www.paloaltonetworks.com/cyberpedia/logs-vs-metrics-vs-traces#traces?ts=markdown) * [How Do Logs, Metrics and Traces Work Together?](https://www.paloaltonetworks.com/cyberpedia/logs-vs-metrics-vs-traces#how?ts=markdown) * [Logs vs. Metrics vs. Traces: Which Should You Use?](https://www.paloaltonetworks.com/cyberpedia/logs-vs-metrics-vs-traces#use?ts=markdown) * [What Role Does OpenTelemetry Play?](https://www.paloaltonetworks.com/cyberpedia/logs-vs-metrics-vs-traces#role?ts=markdown) * [Benefits of Correlating Logs, Metrics and Traces](https://www.paloaltonetworks.com/cyberpedia/logs-vs-metrics-vs-traces#benefits?ts=markdown) * [Best Practices for Managing Logs, Metrics and Traces](https://www.paloaltonetworks.com/cyberpedia/logs-vs-metrics-vs-traces#best?ts=markdown) * [Logs vs. Metrics vs. Traces: Key Differences FAQs](https://www.paloaltonetworks.com/cyberpedia/logs-vs-metrics-vs-traces#faqs?ts=markdown) Logs, metrics and traces are complementary forms of telemetry used in [observability](https://www.paloaltonetworks.com/cyberpedia/what-is-observability?ts=markdown). Logs record individual events, metrics summarize numerical behavior over time and traces follow requests as they move through distributed systems. In practical terms, metrics indicate that system behavior changed, traces show where a transaction slowed or failed and logs provide detailed evidence about what occurred. Organizations correlate all three signals to understand application health, investigate failures and improve service reliability. Key Points * **Logs provide event detail**: Logs capture timestamped events, errors, actions and diagnostic context. \* **Metrics show numerical trends**: Metrics track measurements such as request rates, latency, errors and resource utilization over time. \* **Traces follow individual requests**: Traces show the path and duration of a transaction across services and dependencies. \* **Each signal answers different questions**: Metrics show what changed, traces show where it changed and logs help explain why. \* **Correlation creates observability**: Shared timestamps, trace IDs and resource attributes connect signals into a usable troubleshooting workflow. \* **Telemetry requires governance**: Sampling, aggregation, retention and label controls help manage cost and data volume. ![Comparison of logs, metrics and traces showing the data each observability signal records and its primary use.](https://www.paloaltonetworks.com/content/dam/pan/en_US/images/cyberpedia/logs-vs-metrics-vs-traces/logs-vs-metrics-vs-traces.webp "Comparison of logs, metrics and traces showing the data each observability signal records and its primary use.") ***Figure 1**: Logs vs. Metrics vs. Traces* ## Logs vs. Metrics vs. Traces at a Glance | Signal | What it Records | Questions it Answers | Best Uses | Common Limitation | | **Logs** | Discrete, timestamped events | What event or error occurred? What changed? | Debugging, auditing, forensics and detailed diagnostics | High volume and inconsistent formats can make logs expensive or difficult to search | | **Metrics** | Numerical measurements aggregated over time | Is the system healthy? Is performance changing? | Dashboards, alerts, capacity planning and SLO tracking | Aggregation may hide the details of individual failures | | **Traces** | The end-to-end path and timing of individual requests | Where did a request slow down or fail? | Dependency analysis, latency investigation and distributed root-cause analysis | Full-fidelity tracing may require substantial storage and processing | | **Combined signals** | Correlated operational context | What happened, where did it happen and why? | End-to-end troubleshooting and incident response | Requires consistent instrumentation, metadata and correlation | |----------------------|-------------------------------------------------------|-------------------------------------------------|--------------------------------------------------------------------------------|-------------------------------------------------------------------------------------| ## What Are Logs? Logs are timestamped records of events generated by applications, infrastructure, cloud platforms and security controls. A log entry might describe a user action, authentication result, application exception, database error, configuration change or system event. A typical application log might include: * Timestamp * Service or application name * Severity level * Event or error message * User, session or request identifier * Host, container or workload metadata * Trace and span identifiers Logs provide more granular context than metrics. They are particularly useful when an engineer needs to inspect the exact circumstances surrounding an error or reconstruct a sequence of events. However, logs can become difficult to manage when systems produce large volumes of duplicated, unstructured or low-value entries. Structured logs with stable fields are generally easier to search, correlate, route and govern than unstructured text. ### When Should You Use Logs? Use logs for: * Application debugging * Error and exception investigation * Security and forensic analysis * Audit trails * Configuration and deployment records * User activity investigation * Rare events and edge cases * Detailed evidence associated with a trace Logs answer questions such as, "Which exception occurred?" or "What configuration change preceded the failure?" ## What Are Metrics? Metrics are numerical measurements collected and evaluated over time. Common examples include request count, error rate, response latency, CPU utilization, memory consumption, queue depth, throughput and service availability. Metrics normally include a name, value, timestamp and a controlled set of attributes or labels. For example, an HTTP request metric might be segmented by service, region, endpoint or response status. Because metrics are compact and easy to aggregate, they work well for: * Dashboards * Alerts * Performance baselines * Capacity planning * Trend analysis * Service-level indicators * Service-level objectives Metrics can quickly show that an application's error rate increased or that its latency exceeded an established threshold. They usually cannot explain the precise cause of an individual failure. Teams should also control the number and variability of metric labels. Unbounded values such as user IDs, request IDs or session IDs can create[high-cardinality telemetry](https://www.paloaltonetworks.com/cyberpedia/what-is-high-cardinality?ts=markdown), increasing storage costs and slowing queries. ### When Should You Use Metrics? Use metrics for: * Continuous service-health monitoring * Alerting and anomaly detection * Tracking latency, traffic, errors and saturation * Capacity and resource planning * SLI and SLO measurement * Comparing performance over time * Executive and operational dashboards Metrics answer questions such as, "Is latency increasing?" and "Is the error rate above the SLO?" For more information about reliability measurements, read [What Are SRE Fundamentals?](https://www.paloaltonetworks.com/cyberpedia/sre-fundamentals-sla-vs-slo-vs-sli?ts=markdown) ## What Are Traces? [Distributed tracing](https://www.paloaltonetworks.com/cyberpedia/what-is-distributed-tracing?ts=markdown) follows an individual request or transaction as it moves through applications, services and infrastructure. A trace is composed of spans. Each span represents a unit of work, such as: * An API request * A service call * A database query * Queue processing * Authentication * Model inference * A call to an external dependency The spans collectively show the request's end-to-end path, the order of operations and the time spent in each component. This makes traces particularly useful in microservices and distributed architectures, where a single transaction may cross many independently operated services. Traces can reveal that a request failed in a particular service or spent most of its time waiting for a database, API or message queue. They provide transactional structure but may require sampling in environments with very high request volumes. ### When Should You Use Traces? Use traces for: * Following requests across microservices * Identifying latency bottlenecks * Understanding service dependencies * Investigating failed transactions * Evaluating database and API performance * Finding where errors propagate * Comparing application behavior before and after deployments Traces answer questions such as, "Which services handled this request?" and "Where did the delay begin?" ![Observability troubleshooting workflow using a metric alert, distributed trace and correlated logs to identify the root cause of an application issue.](https://www.paloaltonetworks.com/content/dam/pan/en_US/images/cyberpedia/logs-vs-metrics-vs-traces/correlated-troubleshooting-workflow.webp "Observability troubleshooting workflow using a metric alert, distributed trace and correlated logs to identify the root cause of an application issue.") ***Figure 2**: An observability troubleshooting workflow uses a metric alert, distributed trace and correlated logs to identify the root cause of an application issue.* ## How Do Logs, Metrics and Traces Work Together? Logs, metrics and traces produce the greatest value when teams can move between them without manually searching separate data silos. Consider an online checkout application: 1. A metric detects the symptom. An alert shows that checkout latency has exceeded its service-level objective. 2. A trace isolates the bottleneck. An exemplar or linked trace shows that most of the delay occurred in the inventory service. 3. Logs explain the failure. Logs from the affected service reveal repeated connection-pool errors following a deployment. 4. Context confirms the cause. Deployment metadata connects the errors to a recent application release. 5. The team verifies recovery. Metrics confirm that latency and error rates returned to normal after remediation. This workflow illustrates the basic relationship: * Metrics show what changed. * Traces show where the change affected a request. * Logs help explain why the change occurred. Shared timestamps, service names, trace IDs, span IDs, deployment data and resource attributes make this correlation possible. ## Logs vs. Metrics vs. Traces: Which Should You Use? The appropriate signal depends on the question being investigated. | Operational Question | Best Starting Signal | | Is the application healthy? | Metrics | | Is latency increasing? | Metrics | | Which service caused the delay? | Traces | | Where did a request fail? | Traces | | What exception occurred? | Logs | | Who changed the configuration? | Logs | | Is an SLO being met? | Metrics | | What happened during one customer transaction? | Traces and logs | | What caused the incident? | Metrics, traces and logs together | |------------------------------------------------|-----------------------------------| Organizations should not force every type of data into a single signal. Dynamic identifiers such as request IDs, user IDs and session IDs generally belong in logs or traces rather than metric labels. Conversely, teams should not rely on raw logs alone for high-level service-health monitoring. Recalculating every operational trend from log events can be slower and more expensive than using purpose-built metrics. The service's importance, architecture, traffic volume, compliance requirements and investigative needs should determine the telemetry mix. ## What Role Does OpenTelemetry Play? [OpenTelemetry](https://www.paloaltonetworks.com/cyberpedia/what-is-open-telemetry?ts=markdown) is a vendor-neutral framework for generating, collecting, processing and exporting telemetry. OpenTelemetry helps standardize instrumentation across different programming languages, applications and infrastructure. Its APIs, software development kits and collectors can create consistent attributes and propagate trace context across service boundaries. OpenTelemetry does not replace an observability backend. Instead, it provides a common way to instrument services and transport logs, metrics and traces to compatible analysis platforms. Standardized instrumentation can make signals easier to correlate while reducing dependence on proprietary collection agents. ## Benefits of Correlating Logs, Metrics and Traces Correlated telemetry can help organizations: * Reduce time spent identifying incident causes * Detect performance degradation earlier * Follow failures across distributed services * Improve application and infrastructure reliability * Measure and maintain service-level objectives * Understand the effect of deployments and configuration changes * Reduce handoffs between development, operations and security teams * Prioritize high-value telemetry while controlling cost This correlation is especially important in [cloud native observability](https://www.paloaltonetworks.com/cyberpedia/what-is-cloud-native-observability?ts=markdown), where containers, Kubernetes workloads, APIs and microservices may be temporary, distributed and constantly changing. ## Best Practices for Managing Logs, Metrics and Traces ### 1. Define the questions the telemetry must answer. Start with critical services, risks and operational use cases. Collect data that supports detection, troubleshooting, reliability or compliance rather than collecting everything indefinitely. ### 2. Establish a telemetry contract. Define standards for signal names, required fields, resource attributes, severity levels, ownership and sensitive-data handling. Consistent conventions make telemetry easier to combine and govern. ### 3. Use consistent resource attributes. Apply common service, environment, region, workload and deployment identifiers across logs, metrics and traces. ### 4. Propagate trace context. Include trace and span identifiers in relevant logs. This allows engineers to move from a trace to the detailed events associated with a specific request. ### 5. Control metric cardinality. Reserve metric labels for bounded values such as service, region or response category. Keep rapidly changing identifiers in logs or traces. ### 6. Apply intelligent sampling. Use trace-sampling policies that preserve errors, slow transactions and representative normal traffic. Sampling can control data volume without eliminating high-value investigative evidence. ### 7. Set signal-specific retention policies. Not every signal requires the same retention period. Base retention on operational value, investigation windows, compliance requirements and cost. ### 8. Protect sensitive information. Filter or redact passwords, access tokens, personal information and other sensitive data before telemetry is stored or exported. ### 9. Monitor the telemetry pipeline. Track dropped data, processing delays, failed exports and instrumentation gaps. An observability system cannot provide reliable answers if its own pipeline is incomplete. ### 10. Connect telemetry to operational context. Correlate signals with deployments, configuration changes, service ownership, topology and incident records. Understanding what changed is often as important as the telemetry itself. Effective [observability and monitoring](https://www.paloaltonetworks.com/cyberpedia/observability-vs-monitoring-key-differences?ts=markdown) depend on how quickly teams can convert telemetry into action---not on the raw volume of data collected. ## Logs vs. Metrics vs. Traces: Key Differences FAQs ### What are the three primary observability signals? The three primary observability signals are logs, metrics and traces. Logs record detailed events, metrics measure numerical behavior over time and traces follow individual requests through distributed systems. ### Which is more important: logs, metrics or traces? None is universally more important. Metrics efficiently monitor service health, traces explain distributed request paths and logs provide detailed diagnostic evidence. Critical applications typically require a governed combination of all three. ### What is the main difference between logs and metrics? Logs record individual events with detailed context. Metrics represent numerical measurements aggregated over time. Logs help investigate specific incidents, while metrics are generally better suited to dashboards, alerts and trend analysis. ### What is the difference between logs and traces? Logs document events generated by a system, while traces connect operations belonging to the same request or transaction. A trace shows the request's path; correlated logs provide detailed evidence from the services along that path. ### What is the difference between metrics and traces? Metrics summarize system behavior across many events or requests. Traces capture the behavior of individual requests. Metrics can identify increasing latency, while traces can show which service or operation caused it. ### Can logs replace metrics? Logs can be converted into metrics, but this may be inefficient for continuous monitoring. Native metrics are generally better for dashboards, alerting, capacity planning and SLO measurement. ### Can traces replace logs? Not completely. Traces show request paths, service dependencies and timing, but they may not include the complete diagnostic, audit or security context available in logs. ### Why not collect every trace? Collecting every trace may create significant storage and processing costs at high request volumes. Sampling policies can preserve errors, slow requests and representative healthy traffic while controlling volume. ### What is an exemplar? An exemplar connects an aggregated metric observation to a representative trace. It helps an engineer move from a metric trend or alert to a specific transaction for further investigation. ### How does OpenTelemetry relate to logs, metrics and traces? OpenTelemetry provides vendor-neutral APIs, SDKs and collection components for producing, processing and exporting logs, metrics and traces. It helps organizations standardize telemetry and maintain consistent context across systems. ![Share page on facebook](https://www.paloaltonetworks.com/etc/clientlibs/clean/imgs/resources/facebook-circular-icon.svg) ![Share page on linkedin](https://www.paloaltonetworks.com/etc/clientlibs/clean/imgs/resources/linkedin-circular-icon.svg) [![Share page by an email](https://www.paloaltonetworks.com/etc/clientlibs/clean/imgs/resources/email-circular-icon.svg)](mailto:?subject=Logs%20vs.%20Metrics%20vs.%20Traces%3A%20Key%20Differences&body=Compare%20logs%2C%20metrics%20and%20traces%2C%20including%20what%20each%20observability%20signal%20records%2C%20when%20to%20use%20it%20and%20how%20they%20work%20together%20to%20troubleshoot%20issues.%20at%20https%3A//www.paloaltonetworks.com/cyberpedia/logs-vs-metrics-vs-traces) Back to Top [Previous](https://www.paloaltonetworks.com/cyberpedia/what-is-ai-observability?ts=markdown) What Is AI Observability? 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