To lower hosting costs and service prices, database-as-a-service (DBaaS) providers strive to maximize cluster utilization without negatively affecting their users’ service experience. Some of the most effective approaches for increasing service efficiency result in the over-booking of the cluster with user databases. For instance, one approach is to reclaim cluster capacity from a database when it is idle, temporarily re-using the capacity for some other purpose, and over-booking the cluster’s resources. Such approaches are largely driven by policies that determine when it is prudent to temporarily reclaim capacity from an idle database. In this paper, we examine policies that inherently tune the system’s idle sensitivity. Increased sensitivity to idleness leads to aggressive over-booking while the converse leads to conservative reclamation and lower utilization levels. Aggressive over-booking also incurs a “reserve” capacity cost (for when we suddenly “owe” capacity to previously idle databases.) We answer these key questions in this paper: (1) how to find a “good” resource reclamation policy for a given DBaaS cluster of users; and (2) how to forecast the needed near-term reserve capacity. To help us answer these questions, we used production user activity traces from Azure SQL DB and built models of an over-booking mechanism. We show that choosing the right policy can substantially boost the efficiency of the service, facilitating lower service prices via lower amortized infrastructure costs.