Metal additive manufacturing has emerged as a revolutionary technology for the fabrication of high-complexity components. However, this technique presents unique challenges related to the structural integrity and final strength of the parts produced due to inherent defects, such as porosity, cracks, and geometric deviations. These defects significantly impact the fatigue life of the material by acting as stress concentrators that accelerate failure under cyclic loading. On the one hand, this type of model is very complicated in its approach, since, even with encouraging results, the complexity of the calculation with these variables makes it difficult to obtain a simple result that allows for a generalized interpretation. On the other hand, using more familiar methods, it is possible to qualitatively guess the behavior that helps obtain results with better applicability, even at limited levels of precision. This paper presents a simplified finite element method combined with a statistical approach to model the presence of porosity in metal components produced by additive manufacturing. The proposed model considers a two-dimensional square plate subjected to tensile stress, with randomly introduced defects characterized by size, shape, and orientation. The percentage of porosity that affects each aspect determines the adjustment of the mechanical properties of finite elements. A series of simulations were performed to generate multiple models with random defect distributions to estimate maximum stress values. This approach demonstrates that complex models are not always necessary for a preliminary practical estimate of the effects of new manufacturing techniques. Furthermore, it demonstrates the potential for the extension of frugal computational techniques, which aim to minimize computational and experimental costs in the engineering field. The article discusses future research directions, particularly those related to potential business applications, including commercial uses. This follows a discussion of the existing limitations of this study.