Earlier in my career as a tester, I assumed logging and observability were basically the same thing, just different names for checking whether something had broken.
As I spent more time exploring them I realized they solve different problems. Logging records events that have already happened, while observability helps explain why those events happened and how the system reached that state. That distinction becomes increasingly important as systems grow in complexity.
A log entry might report a NullPointerException, but it rarely answers the questions that follow. Which release introduced the issue? Which commit caused it? How many users are affected? Which service failed first? What sequence of events led to the failure?
This is where platforms such as Sentry demonstrate the difference. Similar errors are grouped into a single issue instead of appearing as hundreds of separate alerts. Errors are linked to the relevant commit and source code, while traces, profiling, and session replay provide the context needed to understand what happened before the failure occurred.
The distinction is simple. Logging answers what happened. Observability helps answer why it happened.
Logs remain an essential part of every system, but logs alone rarely provide enough context for modern distributed applications. As software systems continue to grow, understanding system behavior becomes just as important as collecting data.
How do you distinguish between logging and observability when investigating production issues?
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