The manufacturing process based fatigue strength assessment of cast components is a challenging task that is continuously driven by lightweight design concepts and environmental requirements. The fatigue strength of cyclically loaded components is primarily affected by notches or defects which are statistically distributed within the component. A local fatigue assessment is usually possible only for extremal imperfections. Therefore, this work pursues a layer-by-layer, statistical analysis of microporosity to improve the fatigue strength calculation of cast components. At first, to analyse the statistical size effect of non-connected highly stressed volumes, a sample geometry for the concurrent loading of two geometrically separated, highly stressed volumes is presented, characterized, and a damage hypothesis for this statistical size effect established. Since the microstructure in the highly stressed volumes can differ significantly, the influence of secondary dendrite arm spacing on short and long crack growth is thoroughly investigated, and a three-dimensional Newman diagram is established. The interaction between highly stressed volumes and secondary dendrite arm spacing as a representative microstructural feature is discussed. A layer-based assessment of fatigue strength is conducted based on computed tomography, evaluating defect distributions layer by layer. Based on the determined size parameters of the microstructure and cyclic loading for each defined layer, the layer-based endurance limit can be calculated as probability value. Thus, a conservative prediction of fatigue strength within a range of 12% for different layers in samples of cast aluminium alloy is demonstrated. Furthermore, the component's service life is significantly influenced by the residual stress state. An elastic-plastic model to account for stabilized residual stresses in the surface layer is developed based on the analysis of vibratory finished and polished samples, considering the cyclic rearrangement of mean stress state for each assessed layer. The layer-based fatigue strength assessment methodology was established for the cast aluminium alloy AlSi8Cu3 and validated for the cast steel alloys G21Mn5+N and G12MnMo7-4+QT. For all examined cast materials, the layer-based methodology accounts for a prediction of long-life fatigue strength within a range of approximately 10%. Thus, an engineering-applicable, comprehensive assessment concept is presented, enabling layer-based, manufacturing process based design of cast components.