Infrastructure-as-a-system compute clouds such as Amazon’s EC2 allow users to pay a flat hourly rate to run their virtual machine (VM) on a server providing some combination of CPU access, storage, and network. But not all VM instances are created equal: distinct underlying hardware differences, contention, and other phenomena can result in vastly differing performance across supposedly equivalent instances. The result is striking variability in the resources received for the same price. We initiate the study of customer-controlled placement gaming: strategies by which customers exploit performance heterogeneity to lower their costs. We start with a measurement study of Amazon EC2. It confirms the (oft-reported) performance differences between supposedly identical instances, and leads us to identify fruitful targets for placement gaming, such as CPU, network, and storage performance. We then explore simple heterogeneity-aware placement strategies that seek out better-performing instances. Our strategies require no assistance from the cloud provider and are therefore immediately deployable. We develop a formal model for placement strategies and evaluate potential strategies via simulation. Finally, we verify the efficacy of our strategies by implementing them on EC2; our experiments show performance improvements of 5% for a real-world CPU-bound job and 34 % for a bandwidth-intensive job.