Clustering of data points is a fundamental tool in data analysis. We consider points $X$ in a relaxed metric space, where the triangle inequality holds within a constant factor. A clustering of $X$ is a partition of $X$ defined by a set of points $Q$ ({\em centroids}), according to the closest centroid. The {\em cost} of clustering $X$ by $Q$ is $V(Q)=\sum_{x\in X} d_{xQ}$. This formulation generalizes classic $k$-means clustering, which uses squared distances. Two basic tasks, parametrized by $k \geq 1$, are {\em cost estimation}, which returns (approximate) $V(Q)$ for queries $Q$ such that $|Q|=k$ and {\em clustering}, which returns an (approximate) minimizer of $V(Q)$ of size $|Q|=k$. When the data set $X$ is very large, we seek efficient constructions of small samples that can act as surrogates for performing these tasks. Existing constructions that provide quality guarantees, however, are either worst-case, and unable to benefit from structure of real data sets, or make explicit strong assumptions on the structure. We show here how to avoid both these pitfalls using adaptive designs. The core of our design are the novel {\em one2all} probabilities, computed for a set $M$ of centroids and $\alpha \geq 1$: The clustering cost of {\em each} $Q$ with cost $V(Q) \geq V(M)/\alpha$ can be estimated well from a sample of size $O(\alpha |M|\epsilon^{-2})$. For cost estimation, we apply one2all with a bicriteria approximate $M$, while adaptively balancing $|M|$ and $\alpha$ to optimize sample size per quality. For clustering, we present a wrapper that adaptively applies a base clustering algorithm to a sample $S$, using the smallest sample that provides the desired statistical guarantees on quality. We demonstrate experimentally the huge gains of using our adaptive instead of worst-case methods.