Observability is the broader capability of understanding the internal state of your system from its outputs. It encompasses tracing, metrics, and logging. Tracing is a specific observability technique that records the flow of a request through your system, preserving causal relationships between operations. In the context of LLM applications, tracing is the most important observability tool because it captures the full context of each request — prompts, responses, tool calls, and their relationships.

Application tracing records the complete lifecycle of a request as it flows through your system. Each trace captures every operation — LLM calls, retrieval steps, tool executions, and custom logic — along with timing, inputs, outputs, and metadata. This gives you full visibility into what happened during each request, enabling debugging, performance optimization, and quality monitoring.

Langfuse is purpose-built for LLM applications, which means it natively understands LLM-specific concepts like token usage, model parameters, prompt/completion pairs, and evaluation scores. Unlike general-purpose APM tools, Langfuse provides features specific to AI engineering: LLM-as-a-Judge evaluation, prompt management, experiments and datasets, and custom dashboards. It's also open source and can be self-hosted.

No. Langfuse SDKs send tracing data asynchronously in the background. Trace events are queued locally and flushed in batches, so your application's response time is not affected. See queuing and batching for details.