Mini Batch K-means (cite{Sculley2010}) has been proposed as an alternative to the K-means algorithm for clustering massive datasets. The advantage of this algorithm is to reduce the computational cost by not using all the dataset each iteration but a subsample of a fixed size. This strategy reduces the number of distance computations per iteration at the cost of lower cluster quality. The purpose of this paper is to perform empirical experiments using artificial datasets with controlled characteristics to assess how much cluster quality is lost when applying this algorithm. The goal is to obtain some guidelines about what are the best circumstances to apply this algorithm and what is the maximum gain in computational time without compromising the overall quality of the partition.