The onset of Alzheimer’s Disease (AD) may begin up to 20 years before clinical symptoms are apparent. It is a challenge for clinicians to treat, as the best medical outcome is temporarily slowing the onset of the disease before it becomes ultimately fatal. Due to economic and social pressure AD places on the affected individual and their families, it is particularly burdensome to low-to-middle income countries (LMCIs). Thankfully, cutting-edge deep learning (DL) models are being developed to predict the diagnosis of AD before clinical symptoms manifest. These DL models most commonly use the biomarker fluorodeoxyglucose (FDG), although amyloid-PET (florbetapir) has also become prevalent. FDG is the more accessible and affordable biomarker, whereas, florbetapir detects the amyloid plaque that is theorized to be directly responsible for the neuronal death in AD and may be the better detector of the disease. To determine the most practical biomarker for LMICs, we utilized low-resolution datasets of FDG-PET and amyloid-PET provided by the Alzheimer’s Disease Neuroimaging Initiative to train two DL models with a 3D image classification framework. The FDG-PET and amyloid-PET models resulted in AUCs of 0.919 and 0.891 on their respective test sets. We conclude the difference of DL performance between the two biomarkers trained on low-resolution PET data is negligible. Thereby, as the modestly priced and universal biomarker, FDG-PET is the practical option for LMICs and other financially vulnerable communities for the prediction of a future AD diagnosis.