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A checkout service can run in a public cloud, call a private-cloud API, and depend on an on-premises database. When checkout slows, engineers need to trace the problem across all three—not just inspect the cloud dashboard. Cloud observability is the practice of making a system’s state understandable through its outputs, wherever its software and infrastructure run.
What is cloud observability?
In control theory, observability describes how well a system’s internal state can be inferred from its external outputs. The CNCF TAG Observability whitepaper applies that idea to software operations: teams use evidence from applications and their supporting infrastructure to understand what is happening and decide what to do. The whitepaper is version 1.0, dated October 2023. Read the CNCF Observability Whitepaper.
The word “cloud” often brings to mind dynamic, cloud-native services, but the operational question is broader: can the people responsible for a service determine its health and explain its behavior across the whole system? That system might include public or private cloud resources, on-premises hardware, and connections between them. Observability is therefore not a synonym for a particular dashboard or hosting model.
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It also starts with questions, not with collecting everything. An engineer might need to know which dependency is slowing checkout, whether a recent release changed error rates, or whether a host’s resource pressure is affecting requests. Those questions give teams a reason to instrument the software, choose useful signals, and decide what to retain and alert on. The CNCF whitepaper warns that purposeless collection can increase costs and alert fatigue.
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How is observability different from monitoring?
Monitoring typically tracks known conditions with predefined metrics, dashboards, and alerts—for example, whether error rates exceed a threshold. Observability uses a system’s outputs to investigate its internal state, including questions that were not anticipated when a dashboard or alert was configured. The two practices work together: monitoring can surface a symptom, while observability helps an engineer explore its causes and scope.
Neither term guarantees that an incident will be diagnosed automatically. Useful answers depend on what a team has instrumented, how well its signals can be connected, and whether people can interpret the evidence. Design decisions can affect that work early: teams may add instrumentation in source code or use automated instrumentation where appropriate.
How do logs, metrics, and traces work together?
Each signal gives a different view of system behavior. Correlating them helps connect a service-level symptom to a particular request, event, or infrastructure condition.
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- Logs are records of particular events, often with timestamps and contextual fields. They can provide detail about what an application or host did at a point in time.
- Traces follow a request or operation through multiple services and dependencies. They help show where time was spent or where an error occurred along the path.
- Structured events capture discrete occurrences in a consistent format, making them easier to query and relate to other telemetry.
- Profiles show where a program spends resources, which can help locate costly code paths.
- Crash dumps preserve diagnostic state when software fails. They can be valuable in specific failure investigations, though they are not a substitute for the other signals.
Metrics might reveal that checkout latency rose after a deployment; a trace can show that requests are waiting on a private-cloud API; logs and structured events can add the relevant error context; and a profile may help explain unusually high CPU use. This example illustrates how signals can complement one another, not a requirement to collect every signal for every service.
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What is OpenTelemetry?
OpenTelemetry (often shortened to OTel) is an open-source project that provides specifications, APIs, language-specific implementations, and a Collector for producing, receiving, processing, and exporting telemetry. It was formed in May 2019 by merging OpenTracing and OpenCensus. OpenTelemetry reports that the CNCF graduated the project in May 2026; its project update was modified July 15, 2026. See OpenTelemetry’s project history and status update.
The project’s specifications cover traces, metrics, and logs, and the project update notes that profiling has been added as a signal as the ecosystem evolves. A shared instrumentation and collection approach can make it easier to route telemetry to compatible backends, but adopting OTel does not select a backend, settle data-retention policy, create useful alerts, or remove integration and governance work. Teams still need to decide what to instrument, how to process and export data, and who owns the resulting operations.
Do I need observability for on-premises systems?
Yes, when on-premises software or infrastructure affects a service your team needs to operate. A customer-facing cloud service can depend on a private network, a data center database, or older software that does not move at the same pace as cloud-native components. If the investigation stops at the cloud boundary, an important part of the request path can remain invisible.
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Cloud-native systems can make the task more demanding because components and workloads may change rapidly, but that does not make observability exclusive to them. The useful scope is the service and its dependencies: include the infrastructure and applications that can affect its behavior, regardless of where they run. Instrumentation methods may differ by system, and coverage should be assessed across both application state and underlying infrastructure health.
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Why do teams end up with multiple observability tools?
Telemetry needs span applications, infrastructure, and signals, while organizations may inherit tools from different teams or deployment environments. Integration and configuration then become operational work in their own right. A CNCF post published May 6, 2026, reported findings from a Middleware survey conducted in February 2026 with 407 practitioners across more than 20 industries. These are survey results, not universal measures of all organizations.
| Finding in the February 2026 Middleware survey | Reported result |
|---|---|
| Organizations using two to three observability tools in parallel | 46.7% |
| Respondents reporting a single unified observability experience | 7.4% |
| Dashboard and alert configuration identified as the top setup challenge | 54% |
| Integration complexity identified as a setup challenge | 46.4% |
| Respondents satisfied with their current setup | 81% |
| Respondents still open to switching | 63% |
| Integration quality cited as the leading reason to consider switching | 55.5% |
| Respondents wanting AI-powered anomaly detection as a built-in capability | 59.5% |
| Respondents wanting human oversight before fully autonomous remediation | 48.3% |
The CNCF’s account of the survey suggests that setup and integration deserve attention alongside feature lists. The AI figures indicate preferences among respondents, not proof that anomaly detection or autonomous remediation improves outcomes. Read the CNCF discussion of the Middleware survey.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Which observability deployment models can fit a mixed environment?
Managed services, self-managed tools, and combinations of the two can coexist across an organization. A CNCF community microsurvey conducted in November–December 2021 among 186 CNCF and Kubernetes community members found respondents using several deployment approaches. The results are historical and community-specific; the options overlap, so the percentages do not add up to 100%.
| Approach reported in the 2021 microsurvey | Respondents reporting use |
|---|---|
| Self-managed observability tools on public cloud | 64% |
| Public-cloud observability as a service | 44% |
| Self-managed observability tools on-premises | 40% |
The same 2022 report said 60% ranked developing best practices as a top observability priority for the coming year, while 53% prioritized a unified view of the technology stack. Those priorities underline that tool deployment alone does not establish a consistent operating practice. Read the CNCF Observability Microsurvey report.
How do I choose an observability platform?
Compare platforms against your services and operating constraints rather than looking for a universal winner. A platform that covers application telemetry but not a critical dependency, or that makes data difficult to route, may not provide the view your team needs.
Quick Recap
- Coverage: Check which applications, infrastructure layers, and signals—metrics, logs, traces, events, and profiles—the platform can handle.
- Interoperability: Determine whether existing tools can consume the telemetry and whether OpenTelemetry collection and export fit your architecture.
- Deployment and control: Decide whether a managed service, self-managed deployment, or combination fits the public-cloud, private-cloud, and on-premises parts of your estate.
- Operational effort: Account for dashboard and alert configuration, data pipelines, integration, ongoing maintenance, and the staff responsible for them.
- Cost and signal policy: Set priorities for what to collect and retain. Estimate the effect of that policy and avoid indiscriminate ingestion that adds cost without helping answer operational questions.
- Human oversight: Decide where automation can help identify anomalies or summarize incidents, and which remediation decisions should remain with an operator.
How should a team put observability into practice?
- Define the service questions. Write down the failure modes and operational questions the team needs to answer, such as where requests slow down or which dependency is driving errors.
- Map the dependencies. Trace the service path across application components, infrastructure, networks, and any external or on-premises systems that can affect it.
- Choose signals for those questions. Decide where metrics, logs, traces, structured events, profiles, or crash dumps will provide useful evidence; do not collect them without a purpose.
- Instrument the system. Add source instrumentation or use appropriate automated instrumentation, and agree on consistent context so signals can be related across components.
- Route and process telemetry. Configure collection and export to the tools your team will use, including checks for coverage and integration across environments.
- Build actionable dashboards and alerts. Tie alerts to conditions that need a response, and ensure dashboards help the on-call team move from a symptom toward relevant evidence.
- Review ownership and cost. Assign responsibility for instrumentation, data pipelines, dashboards, alerts, and retention; revisit what is collected as services and operational needs change.
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