We introduce a temporal Steiner network problem in which a graph, as well as changes to its edges and/or vertices over a set of discrete times, are given as input; the goal is to find a minimal subgraph satisfying a set of $k$ time-sensitive connectivity demands. We show that this problem, $k$-Temporal Steiner Network ($k$-TSN), is NP-hard to approximate to a factor of $k - ε$, for every fixed $k \geq 2$ and $ε> 0$. This bound is tight, as certified by a trivial approximation algorithm. Conceptually this demonstrates, in contrast to known results for traditional Steiner problems, that a time dimension adds considerable complexity even when the problem is offline. We also discuss special cases of $k$-TSN in which the graph changes satisfy a monotonicity property. We show approximation-preserving reductions from monotonic $k$-TSN to well-studied problems such as Priority Steiner Tree and Directed Steiner Tree, implying improved approximation algorithms. Lastly, $k$-TSN and its variants arise naturally in computational biology; to facilitate such applications, we devise an integer linear program for $k$-TSN based on network flows.