Many modern learning problems require comparing probability distributions whose individual observations are themselves probability measures, naturally giving rise to meta-measures in $\mathcal{P}(\mathcal{P}(Ω))$. However, optimal transport distances on this space can be prohibitively expensive to evaluate. We introduce the vertical sliced Wasserstein ($\mathrm{VSW}$) distance, a scalable metric on $\mathcal{P}(\mathcal{P}(Ω))$. Rather than slicing along the transport geometry of $\mathcal{P}(Ω)$, $\mathrm{VSW}$ projects each inner measure using continuous test functions and compares the resulting one-dimensional distributions with one-dimensional Wasserstein distance. We prove that $\mathrm{VSW}$ is a valid metric on $\mathcal{P}(\mathcal{P}(Ω))$, metrizes weak convergence, and admits dimension-independent statistical convergence guarantees. Empirically, $\mathrm{VSW}$ provides high accuracy and efficiency compared with existing distances for meta-measures. We further derive a practical particle gradient for the empirical $\mathrm{VSW}_2^2$ objective and demonstrate its use for gradient-based transport of meta-measures. $\mathrm{VSW}$ provides a simple and scalable alternative for comparing and optimizing distributions of distributions.

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