Abstract: Methods, systems, and apparatuses for using machine learning to simulate changes to virtual warehouse configurations without access to data stored by corresponding virtual warehouses are described herein. A computing device may receive first performance metrics of one or more first queries executed by one or more first virtual warehouses. The computing device may then generate a trained machine learning model to simulate operating parameter changes and predict virtual warehouse query performance metrics. The computing device may then provide performance metrics for one or more second virtual warehouses to the trained machine learning model. Output from the trained machine learning model may comprise performance metric predictions corresponding to a given configuration of a virtual warehouse. Predicted costs associated with those performance metric predictions may be output and, based on user input, the operating parameter of the at least one of the one or more second virtual warehouses may be modified.