Three complementary signals
When an application is slow, three observability signals help you find out why: logs, metrics, and traces. They are complementary, each reveals something different, and used together, connected by request identifiers, they let you pinpoint where latency comes from. Rather than guessing, you can separate time spent in the database, in queues, in application work, and in external API calls. Understanding what each signal shows, and how to tie them together, is the foundation of effective latency investigation.
What metrics reveal
Metrics are aggregated numerical measurements over time, such as request rates, error rates, and latency percentiles. They are excellent for seeing the big picture and spotting when and where something is wrong: a rising latency metric tells you there is a problem and often which service or endpoint is affected. Metrics answer 'is something slow, and roughly where', making them the natural starting point that tells you where to dig deeper with the other signals.
What logs reveal
Logs are detailed records of individual events, what happened, when, and often with context like error messages or timing for a specific operation. Where metrics show trends, logs show specifics: the exact error, the particular slow operation, the detail behind an aggregate number. When a metric flags a problem, logs help you see what actually happened in individual cases. Logs provide the granular evidence that explains the behavior metrics summarize, turning 'something is slow' into a concrete account of events.
What traces reveal
Traces follow a single request as it moves through the system, showing each step and how long each took, the database query, the queue wait, the application processing, the external API call. This is uniquely powerful for latency, because a trace breaks a slow request into its parts and shows exactly where the time went. If metrics say requests are slow and logs show details, a trace pinpoints which stage of a request is the bottleneck, which is often the key to finding latency.
Connect them with request IDs
The glue that makes these signals powerful together is the request ID, a unique identifier attached to a request and carried through its logs, metrics, and trace. With request IDs, you can jump from a slow metric to the relevant trace to the specific logs for the same request, following it end to end. A simple workflow: use metrics to spot the problem, use a trace to see which stage is slow, database, queue, application, or external API, then use logs for the detail. This separates where the time truly goes.
The bottom line
Logs, metrics, and traces each reveal different things, metrics show trends and where to look, logs show specific events and detail, and traces break a single request into timed stages, and request IDs connect them end to end. A workflow of metrics, then traces, then logs lets you separate database time, queue delay, application work, and external API latency. Understanding and combining these signals is how you reliably find where latency comes from.
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See also: What is observability? Explained, How to improve web performance with real user measurements.
Sources
Published date reflects the original event date (2026-10-23). This article is original Skillo editorial written from the sources above; facts were verified in September 2026.
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