However, you may also have opportunities to be selective in which predictors you include (e.g., if you have multiple variables that measure a similar construct, you might only include one of them). If you are concerned more with testing a specific statistical model that relates to your research question (e.g., as is common in many social science applications), presumably you have reasons for including particular predictors.If you are concerned more with overall prediction than with statistical significance of individual predictors, then it is probably reasonable to include more predictors than if you are concerned with statistical significance of individual predictors.You may find the discussion of rules of thumb for sample size in multiple regression relevant, as many such rules of thumb make reference to the number of predictors. Rather, bigger sample sizes tend to make it more reasonable to have more predictors and the threshold of how many predictors is reasonable arguably falls on a continuum of reasonableness. In this case the language of 'limits' is not really appropriate. However, I imagine you are talking more about soft limits related to statistical power and good statistical practice. There are limits, such as when you have more predictors than cases, you run into issues in parameter estimation (see the little R simulation at the bottom of this answer). You need to think about what you mean by a 'limit'.

Gpower several multiple regressions