We establish Hoeffding-type concentration inequalities for the low and high tail bounds of sums of exchangeable random variables. Our results exhibit an anti-symmetry in such tail bounds due to the assumption of exchangeability, a generalization of the i.i.d. setting. In contrast to the existing literature on this problem, our result provides an upper tail bound with respect to the largest mean of a distribution in the support of the de Finetti mixing measure, and not the population mean. Equivalently, we establish a lower tail bound with respect to the smallest mean of a distribution in the support of the de Finetti mixing measure. This bridges the gap between finite sample and population means of exchangeable random variables, and distributional means.