This is probably the biggest (only?) advantage of k-means in real problems: it's easy to put new data points into existing clusters. You can do this with mean-s

This is probably the biggest (only?) advantage of k-means in real problems: it's easy to put new data points into existing clusters. You can do this with mean-shift clustering as well. It's not obvious how to do this with HDBSCAN or hierarchical clustering. Maybe you'd have to draw some kind of boundary around each cluster after fixing the parameters.