Should we used k-means++ instead of k-means?

The k-means++ algorithm helps in two following points of the original k-means algorithm:

1. The original k-means algorithm has the worst case running time of super-polynomial in input size, while k-means++ has claimed to be O(log k).
2. The approximation found can yield a not so satisfactory result with respect to objective function compared to the optimal clustering.

But are there any drawbacks of k-means++? Should we always used it instead of k-means from now on?