Decision tree-based Bagging and Random Forest

I am new to Machine Learning and would like to know is it always true that decision tree-based bagging has worse predictive performance and is slower running time than random forest?

Most of the differences will be probably due to the fact that Random Forests routines will already be designed with the ability of tune some of the hyper-parameters that can associated with random sampling the original sample (e.g. proportions of the $$p$$ features to use) while when using standard bagging we would (almost always) assume that all of the $$p$$ the features available are to be included. Otherwise most of the other options (e.g. what would be the minimal data per leaf) would equally affect the (standard) decision trees that are used as base learners of both methods. Notice that some decision tree implementations by design do some random feature selection themselves (e.g. stopping after the first $$\sqrt{p}$$ features are examined given that one split is a valid based on other criteria), so in such cases the advantage of RF is partially nullified).