We present a sequential acquisition of features and experts framework for datum–wise classification. The goal is to accurately assign labels for each instance, minimizing the acquisition cost of features and experts. An expert uses domain knowledge to make decisions. Starting from a prior belief, features are sequentially acquired in a feature acquisition stage. When this stage terminates, the acquired subset of features is forwarded to an expert acquisition stage, where each expert provides their decision one at a time. At that time, contrary to prior work, the label assignment is reached based on the acquired experts’ decisions thus far. We evaluate the framework’s performance using six real–world datasets and compare it with existing methods. Experiments reveal that the proposed framework increases accuracy up to 56% compared to existing ensemble methods while acquiring 88% fewer features and, more importantly, 80% fewer experts on average.