Today’s software systems are more distributed, dynamic, and complex than ever before. Software is broken down into tens of microservices, running in containers that are created and destroyed in minutes and spread out across different clouds. In such a case, it isn’t enough to know when something goes wrong. Why does it happen? If you have ever thought about what is Observability and how it differs from the monitoring approach that you might be familiar with, this guide covers all you need to know: its definition, its three pillars, its comparison with monitoring, the tools, and how to create your own observability practice.
By the end of this guide, you’ll have a clear understanding of what is Observability and how to apply it to your projects.
What is Observability? A Simple Definition
What is Observability then, in layman's terms? Observability is the capability of comprehending the internal workings of a system through its output data, specifically logs, metrics, and traces. In other words, a system is described as observable when it allows for making new queries about itself without any need for changing the existing code.
This term is borrowed from control theory, where it means the capability of inferring the states of a system from its external outputs. Observability became relevant to software engineering since it perfectly describes the problems faced in distributed systems, when you cannot attach the debugger to production and have to use only the data emitted by the system.
To sum up what is Observability, one might say that it is the approach to instrumenting your systems in such a way that you are able to investigate all problems including unforeseen ones through their telemetry data.
What is Observability vs Monitoring? The Key Differences
Once you have learned about observability and what it means, the first question that comes to mind is how observability differs from monitoring. The two terms are quite closely related, yet they are not the same.
Monitoring refers to the process of gathering, aggregating, and alerting on pre-defined metrics and thresholds. In other words, you decide beforehand which metrics and thresholds to look for, such as CPU usage being over 80%, an error rate over 2%, or the disk being almost full, and the monitoring system lets you know when they occur. Monitoring helps answer the following question: "Is there a problem?"
Observability takes things to another level because it provides you with the data you need to analyze and troubleshoot unexpected problems.
Monitoring vs. Observability
Main concern: The focus of monitoring is “Is the system healthy?”, whereas the concern for observability is “Why is the system acting in such a manner?”
Process: Monitoring makes use of predefined dashboards and alarms, on the other hand, observability allows open-ended exploration.
Handle: Monitoring deals with known failure cases (“known unknowns”) in comparison to observability which helps in exploring unknown failure cases (“unknown unknowns”).
Source: Monitoring depends mostly upon aggregations of metrics, whereas observability encompasses logs, metrics, traces, and context data.
Output: Monitoring normally generates alerts and status dashboards, while observability results in root cause analysis.
Relation: Monitoring is a part of observability, and observability is more extensive than monitoring.
The simplest thing to keep in mind about observability vs. monitoring: monitoring is an action that one performs, while observability is a feature of your systems. Monitoring is an operation which depends on observability, in that if there is no observability, monitoring cannot tell you anything other than the existence of a problem.
It is important to understand observability vs. monitoring since failures today are not as straightforward as they used to be. In a microservices architecture, for instance, a slowdown may result from an incorrectly configured retry policy within one service affecting another due to its saturated database connection pool. There is no dashboard which has anticipated such a scenario beforehand, but observability helps trace it.
The Three Pillars: What is Observability Built On?
To understand what is Observability in technical terms, you need to know its three foundational data types, commonly called the three pillars.
1. Logs
Logs are timestamped records of discrete events that happened in a system: a request was received, a user logged in, an error was thrown, a payment failed. They provide the detailed narrative of what happened.
Good logs are structured (for example, JSON with consistent fields) rather than free-form text, which makes them searchable and aggregatable:
{
"timestamp": "2026-09-28T10:15:32Z",
"level": "error",
"service": "checkout-api",
"trace_id": "4bf92f3577b34da6a3ce929d0e0e4736",
"message": "Payment gateway timeout",
"duration_ms": 5023,
"user_id": "u-48213"
}
Logs are excellent for detail but can be voluminous and expensive to store, which is why the other pillars matter too.
2. Metrics
Metrics are numeric measurements accumulated over time: requests per second, error rate, latency percentiles, memory consumption, queue depth. Metrics are compact, inexpensive to store, and perfectly suited for identifying trends and triggering alerts.
The common approach for selecting metrics is based on the concept of "golden signals" from Google site reliability engineering practices: latency, traffic, errors, and saturation. Measuring these four signals for each service provides most of the information needed to assess its state.
3. Traces
Traces are records of the life cycle of a single request in a distributed application. Trace consists of "spans", and they tell us the exact moments of where the time was spent and where problems occur.
Trace of a checkout request could be something like: API Gateway (5 ms) → Checkout Service (40 ms) → Inventory Service (25 ms) → Payment Service (4,800 ms). Immediately, you understand that the problem is with the Payment Service. Otherwise, without tracing, we would have to correlate logs from four services manually.
When explaining what is Observability, these three pillars are the raw material. The real power comes from correlating them: jumping from a metric spike to the traces that caused it, and from a trace to the exact logs of the failing span.
Beyond the Three Pillars: Modern Observability Signals
As far as logs, metrics, and traces are still the basics, in more modern descriptions of observability, there are some extra signals:
Events – discrete events like deployment, config changes, or feature flag changes which can help to correlate changes and behavior
Profiles – continuous profiling signals about which functions use CPU and memory in production code
Real User Monitoring (RUM) – signals from actual user browsers and devices with the information about how fast pages actually load and have any errors
Synthetic monitoring – automated checks simulating user journey from various locations for catching problems earlier than users do
Business metrics – signals about user signups, conversion on the checkouts, etc.
This list will make teams understand what is Observability not only in technical aspects but also in business aspects.
Why Does Observability Matter? Key Benefits
After learning about Observability in theory, it's time to consider why companies pay for this technology.
1. Quicker Identification and Resolution of Incidents
With Observability, both MTTD (mean time to detect) and MTTR (mean time to resolution) become significantly smaller. No more speculation; engineers analyze the data from the moment the symptoms are detected all the way down to the root cause in just minutes.
2. Better Management of Distributed Systems
In the case of microservices and cloud architectures, incidents are emergent and unpredictable. With the help of Observability, engineers receive the means to investigate interaction between services which are beyond any one person's mental model.
3. Increased Productivity of Developers
Thanks to Observability, developers get information on how their code runs in production and therefore debug code quicker and more confidently and spend less time in war rooms.
4. Early Problem Detection
Good telemetry makes it possible for engineers to notice degradation signals, such as an increase in tail latency, long before the system fails. This makes operations more proactive than reactive.
5. Enhanced Customer Experience
With observability, which connects technical telemetry signals with customer actions, the team can address problems that would otherwise be unnoticed or would cause angry customer calls.
6. Improved and Faster Deployment
Observability makes it possible to use canary release and other progressive deployment strategies by releasing new features gradually and monitoring their performance using telemetry. This is one of the reasons why What is Observability has become a regular part of DevOps and CI/CD discussions.
7. Data-Driven Capacity Planning and Cost Control
Understanding resource usage patterns helps teams right-size infrastructure, avoid over-provisioning, and make informed decisions about scaling.
What is Observability's Role in DevOps and SRE?
What Is Observability is one of the first questions people ask when learning more about DevOps and site reliability engineering because it forms the foundation of multiple best practices of the fields.
Service Level Objectives (SLOs). SREs set goals like "99.9 percent of requests have to be completed within 300 milliseconds." It is observability data that is used to measure SLIs, error budgets, and make unbiased decisions about the balance between reliability and feature development speed.
Incident response. In case of an outage, on-call engineers use dashboards, traces, and logs to triage the problem quickly. The proper observability transforms chaos into a methodical investigation.
Postmortem analysis and continuous improvement. After the outage, telemetry gives the actual sequence of events required for conducting blameless postmortems and gaining insight.
Continuous integration and deployment feedback. Connecting deployment events to telemetry helps to understand precisely which release led to the change in system behavior.
Chaos engineering. The practice of intentionally creating failures to test resilience requires observability data to see the real system behavior.
It is no wonder why the What Is Observability question appears so frequently in DevOps interviews, certification programs, and job postings.
Popular Observability Tools and Platforms
An array of tools is available for implementing observability. In general, they may be classified as open source and commercial products.
Open Source Tools
Prometheus – the leading solution for metric collection and alerting toolkits, particularly in Kubernetes ecosystems
Grafana – a visualization dashboard platform for metrics, logs, and traces
Grafana Loki – a lightweight logging aggregation tool designed to cooperate with Prometheus and Grafana
Jaeger and Zipkin – distributed tracing platforms to trace requests across services
The ELK Stack (Elasticsearch, Logstash, Kibana) – the popular platform for searching and analyzing logs
OpenTelemetry – a vendor-agnostic standard and SDKs for collecting logs, metrics, and traces
Commercial Solutions
Datadog – a full-fledged SaaS platform encompassing infrastructure, APM, logs, and more
New Relic – an observability platform featuring comprehensive APM solutions
Dynatrace – the AI-powered observability platform with auto-discovery and topology features
Splunk Observability Cloud – the combination of metrics, traces, logs, and Splunk's analytical background
Honeycomb – the platform based on high cardinality event data and debugging features
However, regardless of your choice, almost all contemporary organizations rely on OpenTelemetry as an instrumentation standard since it avoids vendor lock-in and allows changing backends without modifying applications.
OpenTelemetry: The Standard Behind Modern Observability
No guide to what is Observability would be complete without discussing OpenTelemetry, often abbreviated OTel. It is an open-source project under the Cloud Native Computing Foundation that provides a unified set of APIs, SDKs, and tools for generating and collecting telemetry data.
The main advantages of OpenTelemetry include:
- Vendor neutrality — instrument your code once and send data to any compatible backend
- Unified signals — one framework for traces, metrics, and logs
- Broad language support — SDKs for Java, Python, Go, JavaScript, .NET, and many others
- Auto-instrumentation — for many frameworks, you can capture useful telemetry with little or no code changes
- The OpenTelemetry Collector — a flexible agent or gateway that receives, processes, and exports telemetry to one or more destinations
A minimal Python example of creating a span looks like this:
from opentelemetry import trace
tracer = trace.get_tracer(__name__)
def process_order(order_id):
with tracer.start_as_current_span("process_order") as span:
span.set_attribute("order.id", order_id)
# business logic here
charge_payment(order_id)
Because of its momentum, OpenTelemetry has become central to any modern answer to what is Observability in practice.
How to Build an Observability Practice: Step by Step
Once you are geared up to take the leap from conceptual understanding to implementation in Observability, below is an outline to help you with your journey.
- Identify the important stuff.
- Define your critical user workflows and services along with SLIs and SLOs to capture the real end-user experience.
- Instrument using OpenTelemetry.
- Implement OpenTelemetry or something similar such that each of your services will generate telemetry in a consistent way.
- Implement structured logging.
- Generate logs in JSON with consistent attributes, along with the trace id to correlate traces with logs.
- Instrument the golden signals.
- Measure latency, traffic, error rate, and saturation for each of your services. Implement distributed tracing.
- Start with the most critical request flows and then increase the coverage.
- Collect and correlate your data. Provide telemetry into a backend system that allows you to flow freely from metrics, to traces, to logs.
- Develop impactful dashboards. Create dashboards centered around health and impact instead of vanity metrics.
- Set alerts that matter. Alert off symptoms that affect users (SLO burn rates) instead of internal noise.
- Correlate your deployments with telemetry. Use annotations on your dashboards for release activity.
- Repeat after each incident. Conduct postmortems for identification of blind spots and improvements in instrumentation.
Following these steps turns the abstract idea of what is Observability into a concrete, repeatable engineering practice.
Best Practices for Observability
After knowing what Observability is all about, here are some of the best practices that will help you leverage its potential:
- Use high cardinality metrics. Using data such as user id, request id, and build version allows slicing data in ways you cannot anticipate.
- Correlate everything via trace id. Spreading trace id across logs, metrics, and traces is what ties pillars together.
- Alert for the symptom, not the cause. Alerting humans to user-impacting issues and sending signals to other places for lower-level signals.
- Manage data size and cost. Sample traces, use retention policies for logs, and aggregate metrics to keep costs low.
- Treat instrumentation as code. Review and test it in addition to version control.
- Make observability a norm for everyone. Instrumentation should be the developer’s responsibility, not only that of operations engineers.
- Avoid leaking sensitive information. Do not log secret data, personally identifiable information, and payment data.
Common Challenges in Adopting Observability
Knowing what is Observability also entails knowledge of the common challenges that are faced in implementing it:
Tooling sprawl – use of multiple, isolated tools leads to the creation of silos and slow investigations
Data volumes and costs – telemetry data can scale exponentially, and uncontrolled costs for ingestion could catch organizations by surprise
Alert fatigue – too many alerts desensitizes people to them, and it becomes risky
Instrumentation inconsistencies – use of different formats and naming conventions by various teams makes correlation difficult
Resistance to change – when observability is considered “the ops team’s problem” and not the engineering team’s responsibility, it becomes less useful
Legacy systems – older systems might not have the required hooks for telemetry
Identifying and resolving these issues upfront goes a long way toward making adoption easier.
Observability in Cloud-Native and Kubernetes Environments
The requirement for observability in Kubernetes is even more pronounced. Here, pods are transient, IP addresses keep changing, and workload scale increases and decreases automatically. Knowing what is Observability here entails using tools that fit dynamic systems.
A typical setup uses Prometheus for metrics, Grafana for visualizations, Loki or ELK stack for logging, and Jaeger or Tempo for tracing, along with OpenTelemetry for instrumentation. Service meshes such as Istio and Linkerd provide automatic telemetry between service instances, enabling one to see traffic, latency, and failure rates between services without having to modify application code. Pod names, namespaces, nodes, and versions of the deployment need to be included in all signals.
The Future of Observability
The future holds several trends that will influence the concept of Observability:
Machine learning powered analysis — machine learning is becoming increasingly common in anomaly detection, alert correlation and providing possible root causes of problems
Unified observability platforms — companies are integrating logging, metrics, tracing and profiling in a single platform to prevent the proliferation of tools
eBPF-powered instrumentation — kernel-level solutions allow deep insight with zero overhead and no modifications of application code
Observability-first development — features are designed with built-in observability and debuggability in mind
Cost-effective observability — efficient sampling, tiered storage and data pipelines make observability more affordable
Convergence with security — the same telemetry can be used for security applications as well
These trends suggest that being aware of what is Observability will be an essential skill for engineers.
Conclusion
But what is Observability then? It's the practice of creating the visibility of your systems to be able to diagnose issues effectively in the case of their occurrence even when there are unexpected failures. While monitoring lets you know about crossing some predetermined threshold, Observability allows you to troubleshoot unexpected events with the help of logs, metrics, tracing and contextual information. As architectures become increasingly distributed and release schedules accelerate, it is crucial for DevOps engineers, Site Reliability Engineers (SRE), and developers to understand the concept of Observability. In case you would like to gain practical skills in monitoring, logging, tracing, CI/CD, containers, and cloud infrastructure in addition to learning what is Observability, you may consider joining a DevOps Course with such training modules.
Frequently Asked Questions About Observability
What is Observability in one sentence?
What is Observability, simply put: it's the ability to understand a system's internal state and diagnose any problem, including unexpected ones, by analyzing the logs, metrics, and traces it produces.
What is the main difference between observability and monitoring?
Monitoring tracks predefined metrics and alerts you when known thresholds are crossed, telling you that something is wrong. Observability gives you the rich, correlated data to explore why it's wrong, including failure modes you never anticipated.
What are the three pillars of observability?
The three pillars are logs (detailed event records), metrics (numerical measurements over time), and traces (end-to-end records of requests moving through distributed services). The most effective practices correlate all three, often using a shared trace ID.
Do I need observability if I already have monitoring?
If your system is simple and failures are predictable, monitoring may be enough. But for microservices, cloud-native, or rapidly changing systems, observability is what allows you to troubleshoot unknown problems quickly, so most modern teams need both.
Which tools should beginners use to learn observability?
A great starting stack is Prometheus and Grafana for metrics and dashboards, Loki or the ELK Stack for logs, Jaeger for tracing, and OpenTelemetry for instrumentation. All are open source and widely used in industry.
