Cell delivery and encapsulation platforms are under development for the treatment of Type 1 Diabetes among other diseases. For effective cell engraftment, these platforms require establishing an immune-protected microenvironment as well as adequate vascularization and oxygen supply to meet the metabolic demands of the therapeutic cells. Current platforms rely on 1) immune isolating barriers and indirect vascularization or 2) direct vascularization with local or systemic delivery of immune modulatory molecules. Supported by experimental data, here a broadly applicable predictive computational model capable of recapitulating both encapsulation strategies is developed. The model is employed to comparatively study the oxygen concentration at different levels of vascularization, transplanted cell density, and spatial distribution, as well as with codelivered adjuvant cells. The model is then validated to be predictive of experimental results of oxygen pressure and local and systemic drug biodistribution in a direct vascularization device with local immunosuppressant delivery. The model highlights that dense vascularization can minimize cell hypoxia while allowing for high cell loading density. In contrast, lower levels of vascularization allow for better drug localization reducing systemic dissemination. Overall, it is shown that this model can serve as a valuable tool for the development and optimization of platform technologies for cell encapsulation.