Abstract: We study the problem of differentially private (DP) stochastic convex optimization (SCO) under the notion of user-level differential privacy. In this problem, there are $n$ users, each contributing $m>1$ samples to the input dataset of the private SCO algorithm, and the notion of indistinguishability embedded in DP is w.r.t. replacing the entire local dataset of any given user. Under smoothness conditions of the loss, we establish the optimal rates for user-level DP-SCO in both the central and local models of DP. In particular, we show, roughly, that the optimal rate is $\frac{1}{\sqrt{nm}}+\frac{\sqrt{d}}{\varepsilon n \sqrt{m}}$ in the central setting and is $\frac{\sqrt{d}}{\varepsilon \sqrt{nm}}$ in the local setting, where $d$ is the dimensionality of the problem and $\varepsilon$ is the privacy parameter. Our algorithms combine new user-level DP mean estimation techniques with carefully designed first-order stochastic optimization methods. For the central DP setting, our optimal rate improves over the rate attained for the same setting in Levy et al. (2021) by $\sqrt{d}$ factor. One of the main ingredients that enabled such an improvement is a novel application of the generalization properties of DP in the context of multi-pass stochastic gradient methods.