What Is Observability? Observability vs Monitoring

Last verified on Nov 8, 2026.

Observability is the ability to understand the internal state of a system by examining the data it emits. OpenTelemetry's definition is that: understanding internal state from outputs, which in software means analyzing telemetry such as traces, metrics, and logs. The practical test is whether you can ask a new question about production and get an answer without shipping new code.

Observability vs Monitoring

Google's SRE book defines monitoring as collecting, processing, aggregating, and displaying real-time quantitative data about a system, such as query counts, error counts, and processing times. It says a monitoring system should address two questions: what is broken, and why. OpenTelemetry's primer frames observability as the ability to ask questions about a system without knowing its inner workings, which helps with novel problems, the "unknown unknowns".

They are not rivals. Monitoring tells you something is wrong, and observability gives you what you need to find out why. The SRE book separates white-box monitoring, based on metrics exposed by the system's internals including logs, from black-box monitoring, which tests externally visible behavior as a user would see it. Both feed alerts. For a measure of how well you meet user expectations, see SLOs.

The Three Pillars

"Three pillars" is shorthand for logs, metrics, and traces. OpenTelemetry's docs call them signals and list baggage as a fourth, with events and profiles in development or proposed. Each answers a different question:

• Metrics: aggregations of numeric data over time. They show that something changed. See golden signals.
• Logs: timestamped messages. They show what happened in a specific event. See structured logging.
• Traces: the path of one request across services. They show where it happened. See distributed tracing.

Having all three is not the same as being observable. The value comes from links between them. OpenTelemetry's logs specification names three: by time of execution, by execution context (trace and span IDs), and by the origin of the telemetry. Put a trace ID in every log line, and a log search can lead straight to the trace.

What Makes a System Observable

1. Instrumentation. The code emits signals. OpenTelemetry says an application is properly instrumented when developers do not need to add instrumentation to troubleshoot an issue. See OpenTelemetry.
2. A shared protocol and conventions. Consistent names and a common wire format such as OTLP make data comparable across services.
3. A backend you can question. Data has to be stored and searchable. OpenTelemetry deliberately leaves this to other tools.
4. Context links. Trace IDs, resource attributes, and timestamps tie signals together.
5. People and process. Dashboards and alerts do not diagnose themselves. Runbooks, ownership, and alert hygiene decide whether the data gets used.

Telemetry covers the data itself. APM is the narrower practice of monitoring application performance to detect issues and find root causes.

Where Teams Go Wrong

• Dashboards as the goal. A wall of charts built from known failure modes is monitoring. It misses the unknown unknowns.
• Signals in silos. Logs in one tool, traces in another, no shared IDs. Each one is useful alone, but you pay in time during an incident.
• No context in logs. Free text without a request or trace ID cannot be joined with anything.
• Collecting everything. Cost climbs with volume. Use sampling and review which data you actually query.

Observability in Sazabi

Sazabi is an AI-native observability and security platform. It normalizes logs, metrics, traces, and webhook events into searchable log records, and an AI agent reads them during an investigation. Its detection includes novelty checks, which catch log messages that never appeared before, where a volume threshold only catches spikes in messages you already know. Sazabi ingests OpenTelemetry metrics (Prometheus metrics through an OpenTelemetry Collector) and OTLP traces through the same intake as your logs. It stores each metric data point and each span as a log record that its agent queries alongside your logs, so there is no separate metrics store to run. To see the kinds of problems it looks for, start with novel events.

Frequently Asked Questions

What is observability in simple terms?

The ability to work out what is going wrong inside a system, and why, using only the data it emits, without having to change the code to find out.

What is the difference between observability and monitoring?

Monitoring collects and displays predefined data, such as dashboards and alerts on known failure modes. Observability is about being able to ask new questions of your data, including about problems you did not predict.

What are the three pillars of observability?

Logs, metrics, and traces. OpenTelemetry calls them signals and also supports baggage, with events and profiles in development or proposed.

Do I need all three pillars?

No, but each answers a different question and they are most useful when linked, for example by putting trace IDs in log lines. Many teams start with logs and add the others as needed.

Sources

1. Observability is the ability to understand the internal state of a system by examining its outputs, typically by analyzing traces, metrics, and logs; a system must be instrumented and the data sent to a backend (accessed Nov 8, 2026)
2. Observability lets you ask questions about a system without knowing its inner workings and handle novel problems, or unknown unknowns; it helps answer why something is happening; a properly instrumented application needs no extra instrumentation to troubleshoot an issue (accessed Nov 8, 2026)
3. Monitoring is collecting, processing, aggregating, and displaying real-time quantitative data about a system; white-box and black-box monitoring; a monitoring system should address what is broken and why (accessed Nov 8, 2026)
4. OpenTelemetry supports traces, metrics, logs, and baggage as signals, with events and profiles under development or proposed (accessed Nov 8, 2026)
5. Logs can be correlated with other telemetry by time of execution, execution context (trace and span IDs), and origin of the telemetry (Resource) (accessed Nov 8, 2026)
6. Sazabi normalizes logs, metrics, traces, and webhook events into one searchable body of log records that its agent reads during an investigation (accessed Nov 8, 2026)