We show that if $\vec X = (X_1, \dots, X_N)$ is a uniform random vector on the unit Euclidean sphere, the empirical CDF of the components of $\sqrt N \vec X = (\sqrt N X_1, \dots, \sqrt N X_N)$ concentrates exponentially rapidly in $N$ around the standard Gaussian CDF $Φ$. More precisely, we find explicit functions $γ$ and $g_\pm$ such that the Kolmogorov-Smirnov distance between the empirical CDF of the components of $\sqrt N \vec X$ and $Φ$ deviates by more than $ε+ γ(t)$ with probability at most $2e^{-2Nε^2} + e^{-Ng_+(t)^2} + e^{-Ng_-(t)^2}$ for $ε> 0$ and $t\in[0,1)$. A weaker but more transparent inequality replacing $γ$ and $g_\pm$ with linear functions is obtained as a corollary. All functions and constants are explicit, so our bounds offer finite-sample guarantees for statistical applications.