One possible option is to consider the number of clusters as an unknown parameter and estimate it along with other parametrs of your model.

Another option is to use a Bayesian nonparametric approach for clustering. This way, you let the data to choose the best number of clusters.

Plotting the residual variance against the number of clusters usually gives good answers.

You can derive K even from your hclust() answer.