Runtime load balancing of work across a clustered computing system involves servers calculating, and clients utilizing, current service performance grades of each instance in the system. A performance grade for an instance is based on performance metrics for that instance, where the computation used may vary by policy. Examples of possible policies include: (a) using estimated bandwidth as a performance grade, (b) using spare capacity as a performance grade, or (c) using response time as a performance grade. Clients distribute work requests across servers in the system as the requests arrive. Work requests can be distributed according to performance grades, and/or flags associated with the performance grades. Automatically and intelligently directing work requests to the best server instances, based on real-time service performance metrics, minimizes the need to manually relocate work within the clustered system.