Tracing vs logging for LLM apps: what's the difference and when to use each

In complex Language Model (LLM) applications, logging alone can be insufficient for diagnosing issues because it captures independent events without illustrating the interconnected steps of a request, which is essential for understanding why a particular run failed. Logs provide discrete, timestamped records of events such as exceptions or infrastructure changes, which are useful for standalone analyses like error reporting or compliance audits. However, they fall short in multi-step LLM processes where tracing becomes crucial. Tracing records the entire path of a request as a series of connected spans, allowing teams to see how each step, from retrieval to final response, transpired and contributed to the outcome. This visibility is particularly valuable in LLM applications where non-deterministic behavior and multi-step executions can lead to different results with the same input. By integrating both logging and tracing, along with tools like Braintrust and OpenTelemetry, teams can achieve comprehensive observability, enabling them to debug issues effectively and evaluate production behavior by connecting structured logs with traces to provide a complete picture of request-level dynamics.