Are three pillars enough for modern observability?

The three pillars remain the foundation, but many teams now add real user monitoring (RUM), continuous profiling, and events as additional signals. What matters more than the number of signals is whether they are correlated — being able to move from a metric spike to the related traces and logs in one motion.

Which pillar should a team adopt first?

Most teams start with logs because every application already produces them, then add metrics for alerting and dashboards, and finally traces once they run enough services that request paths become hard to follow. An OpenTelemetry-based pipeline lets you add signals incrementally without changing backends.

What are the Three Pillars of Observability?

The three pillars of observability are logs, metrics, and traces — the core telemetry signals used to understand, debug, and operate modern software systems.

Observability Fundamentals Last updated: July 8, 2026

The three pillars of observability are logs, metrics, and traces — the three complementary telemetry signals that together describe what a software system is doing. Each answers a different kind of question, and mature observability practices correlate all three.

Logs: what happened

Logs are timestamped, often structured records of discrete events: an error was thrown, a request was served, a configuration changed. They carry the richest context of any signal — stack traces, user IDs, payload details — which makes them the signal of last resort when debugging. Their weakness is volume: verbose services can produce terabytes per day, which is why log management cost and retention strategy matter so much.

Metrics: how much and how fast

Metrics are numeric measurements sampled over time: request rate, error rate, p99 latency, memory usage. They are cheap to store, fast to query, and ideal for dashboards, SLOs, and alerting. Frameworks like the four golden signals describe which metrics matter most. Their weakness is aggregation — a metric tells you that latency spiked, not why, and high-cardinality labels can explode storage costs in some systems.

Traces: where it happened

Traces follow a single request as it flows through a distributed system, recording a span for each operation along the way. They reveal which service, query, or downstream call was responsible for latency or errors — something neither logs nor metrics can show across service boundaries. See distributed tracing for how this works.

The pillars work together

The real power is in correlation. A typical investigation moves across all three: an alert fires on a metric → you open the slow traces from that window → you pivot to the logs of the failing span. If your logs, metrics, and traces live in three disconnected tools, that workflow means copy-pasting timestamps between browser tabs.

The three pillars in OpenObserve

OpenObserve stores logs, metrics, and traces in one platform with shared dashboards and cross-signal correlation, ingesting all three natively via OpenTelemetry — so the pivot from metric to trace to log happens in one place.

Frequently asked questions

: The three pillars remain the foundation, but many teams now add real user monitoring (RUM), continuous profiling, and events as additional signals. What matters more than the number of signals is whether they are correlated — being able to move from a metric spike to the related traces and logs in one motion.
: Most teams start with logs because every application already produces them, then add metrics for alerting and dashboards, and finally traces once they run enough services that request paths become hard to follow. An OpenTelemetry-based pipeline lets you add signals incrementally without changing backends.

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