Quality-diversity (QD) algorithms have exhibited promising results across many domains and applications. However, uncertainty in fitness and behavior estimations of solutions remains a major challenge when QD is used in complex real-world applications. While several approaches have been proposed to improve the performance in uncertain applications, many fail to address a key challenge: determining how to prioritize solutions that perform consistently under uncertainty, in other words, solutions that are reproducible. Most prior methods improve fitness and reproducibility jointly, ignoring the possibility that they could be contradictory objectives. For example, in robotics, solutions may reliably walk at 90% of the maximum velocity in uncertain environments, while solutions that walk faster are also more prone to falling over. As this is a tradeoff, neither one of these two solutions is “better” than the other. Thus, algorithms cannot intrinsically select one solution over the other, but can only enforce given preferences over these two contradictory objectives. In this article, we formalize this problem as the performance-reproducibility tradeoff for uncertain QD (UQD). We propose two new a-priori QD algorithms that find efficient solutions for given preferences over the tradeoffs. We also propose an a-posteriori QD algorithm for when these preferences cannot be defined in advance. Our results show that our approaches successfully find solutions that satisfy given preferences. Importantly, by simply accounting for this tradeoff, our approaches perform better than existing uncertain QD methods. This suggests that considering the performance-reproducibility tradeoff unlocks important stepping stones that are usually missed when only performance is optimized.