Observability spending rises quickly and satisfaction often does not, because the three main signal types get treated as interchangeable. They answer different questions and have very different cost profiles.
Metrics answer: is something wrong?
Numbers aggregated over time — request rate, error rate, latency percentiles, queue depth. Cheap to store, cheap to query, and the right basis for alerting because they describe the system as a whole.
What they cannot tell you is why. A metric shows that error rate tripled at 14:20; it will never show you the failing request.
Traces answer: where is it wrong?
A trace follows one request across every service it touches, with timing per hop. In a distributed system this is the only practical way to find which component is responsible for a latency increase.
Traces are the signal most often missing, and their absence is why distributed debugging so often becomes several people comparing timestamps in separate dashboards.
Logs answer: what exactly happened?
The detail: the specific identifiers, the parameters, the stack trace. Essential once you know where to look, and by far the most expensive signal to store at volume.
Logs are misused as metrics — counting log lines to derive a rate — which is both slow and costly. If you find yourself doing that, emit a metric.
Use them in that order
Alert on metrics. Locate with traces. Explain with logs. Instrumenting in that sequence tends to reduce both cost and mean time to resolution, because each layer narrows the search before the expensive layer is queried.
Correlate, or none of it compounds
A request identifier propagated through every service, present in every log line and every span, is what turns three separate tools into one investigation. Without it you have three tools.
The goal is not maximum data. It is the ability to answer those three questions in order, quickly, at a cost that does not grow faster than the system.