Connect LLM traces to experiment metrics
GrowthBook's beta LLM-tracing integrations connect experiment assignments with the observability data you already collect. Compare prompt or model variations using latency, token cost, errors and evaluation scores, alongside relevant business metrics.
Tie a trace to the variation a user saw
Attach an assignment tag such as gb.exp:support-prompt=variant-b to the trace. The JavaScript tracing plugin collects assignment tags; your application passes them to its tracing SDK before the trace or root span starts. The recorded user or session identifier must match the identifier used for assignment.
The generated integrations target self-hosted Langfuse v3 on ClickHouse and Arize Phoenix backed by Postgres. Langfuse Cloud requires exporting data into a warehouse and adapting the SQL. These trackers are in beta, and their underlying database schemas can change.
For example, test a revised support prompt with helpfulness as a goal and latency or cost as guardrails. Trace metrics tell you how the model behaved; business metrics help assess the product outcome.
Getting started
Follow the LLM experimentation setup guide to tag assignments, connect the appropriate tracing database and validate the generated queries. The public announcement introduces this workflow as a GrowthBook Cloud beta.
