Process analytical technology (PAT) tools are an important part of process monitoring and control in pharmaceutical continuous manufacturing (CM) that help ensure product quality. However, there is hesitancy to adopt PAT due, in part, to the high start-up costs. A portion of the cost is the calibration burden associated with developing an appropriate multivariate data analysis (MVDA) method to extract the desired information from the spectral outputs of spectroscopic PAT tools. This has generated research interest in reduced calibration burden MVDA methods, such as iterative optimization technology (IOT) algorithms, as alternatives to conventional modeling approaches like partial least squares (PLS) regression. The goal of the presented research is to compare the calibration burden of three different MVDA methods (direct IOT, indirect IOT, PLS regression) at two drug loading levels (low and high) of pharmaceutical powder blends in a CM line. The blends were binary mixtures consisting of an active pharmaceutical ingredient and a coprocessed excipient blend. The coprocessed excipient blend was leveraged to reduce formulation complexity and streamline process development, benefiting the application of IOT algorithm. Calibration burden was assessed in terms of time, material, and financial costs. Utilizing a near-infrared spectroscopic PAT tool, it was found that MVDA methods that utilized IOT algorithms demonstrated a notably reduced calibration burden compared to the PLS models, while predicting blend potency with similar accuracy.