Residual stress (RS) is a major processing issue for selective laser melting (SLM) of metal alloys. Postprocessing by way of heat treatment or hot isostatic pressing is usually required for acceptable mechanical properties. In this work, laser shock peening (LSP) treatment on both SLM and cast aluminum A357 alloys are compared with regard to the development of beneficial near-surface compressive RS. Experiments are conducted using high energy nanosecond pulsed laser, together with a fast photodetector connected to a high-resolution oscilloscope and high-speed camera to identify detailed temporal and spatial laser pulse profiles to improve numerical predictions. Constitutive modeling for SLM A357 alloy is performed using finite element simulation and data obtained from X-ray diffraction (XRD) measurements. Since XRD-RS measurements are accompanied with significant machine-reported error, an effective method is introduced to quantify the material constitutive model uncertainty in terms of a joint probability mass function. Conventionally, most constitutive behavior research for LSP involves deterministic material modeling. Predicted RS using deterministic approaches fail to reflect real-world variations in the materials, laser treatment, or RS measurements. A discretized Bayesian inference is used to quantify the rate-dependent plasticity material model parameters as a joint probability function. RS are then characterized as random fields, which provides far greater insight into the practical ability to attain desired residual stresses. Moreover, for identical LSP treatments, it is determined that the material models are significantly different for the SLM and the conventional cast A357 aluminum alloys, resulting in much lower magnitude of compressive RS in the SLM alloy.