Missing data is a common occurrence in data streams, where the feature values and label information are not assumed to be available. While acquiring feature values or labels may be possible, doing so is often associated with monetary and time costs. For instance, acquiring labels is time consuming and may require specialized experts or testing equipment to be available. Similarly, acquiring feature values may require access to restricted and or limited sources with limited availability. If one would be able to maximize performance and only acquire necessary information, budget could be saved. In this work we present a combined approach to acquiring feature values and labels under latency in a stream setting. Our method is tested on four data sets under various latencies and missingness conditions to evaluate its usefulness. We also present an ablation study on the impact of individual factors and their impact on the task performance.