Elbow method for cosine distance
Ok. So what I understood is, that for cosine metrics, I can use both: Sum of squared distances from centroids to vectors that belong to clusters, where the distance can be calculated as euclidian or as cosine (cosine would be probably more precise, but more complicated(thanks to dot product)). The squared distance is only used as optimization, so we don't have to calculate the square root in both euclidian and cosine distance formula.