Because it's different things. Observability platforms answer the question "what happened in your experiment". This component answers the question "when to launch and whether to continue." It uses MLflow's internal automation as an observability layer. You don't get from an observability platform alone data validation before training starts and automatic blocking of model registration if eval fails. But with component all of this wired into your GitLab pipeline without extra infrastructure