Data leaders frequently face a challenging architectural decision: when deploying customer-facing dashboards or embedding analytics into applications, they often spin up a separate real-time serving layer to handle low latency, high concurrency workloads. While this can address performance, it introduces significant complexity, governance risks, and infrastructure overhead. This session challenges the necessity of that serving layer. We will explore how organizations can consolidate their infrastructure by serving high-concurrency end-user analytics directly from the lakehouse with Lakehouse//RT, through real-world customer case studies and benchmarks.