Does ensemble (bagging, boosting, stacking, etc) always at least increase performance?
The short answer is no.

I have worked on several projects that evaluated an ensemble of several classifiers versus the classifies themselves.  In some cases the precision and recall was better with the ensemble, but more often, it was not.  That's not to say it's not worth investigating.  But sometimes, there is one model that does a reasonable job of classifying data, but it can get drowned out in an ensemble.  Perhaps a weighted ensemble might improve the results, but its not a clear cut approach to improving the performance.

In practice, I would try several models and then try an ensemble of the models.  If the ensemble is the best, however you define best, go with it.  But sometimes it is easier to just pick the best base model and then figure out how to tune that model.