We introduce a new learning-based solution for portable database workload performance prediction. The current state of the art addresses performance prediction for individual, static hardware configurations and thus cannot generalize to new platforms without additional training. In this work, we focus on analytical databases that might be deployed on different hardware configurations, possibly offered by various Infrastructureas-a-Service (IaaS) providers in the cloud. Enabling workload performance predictions that can be ported across hardware configurations and IaaS offerings could significantly help cloud users with their service-purchase decisions and cloud providers with their provisioning decisions. Our solution is based on collaborative filtering modeling and prediction. We applied it to lightweight workload fingerprints that model the characteristics and behavior of concurrent query workloads for carefully selected, abstract hardware configurations. Our preliminary results are derived from experiments with TPC-H and TPC-DS benchmarks on the Amazon and Rackspace clouds. They demonstrate that our techniques can predict analytical workload throughput values for diverse hardware platforms with low training overhead and within approximately 30% of the correct figure.