Multi-exponential fitting exemplifies a common occurrence in models of complex systems. In detailed descriptions of complex systems, numerous parameters are often used to fully describe the underlying behavior. When examined by a non-specific measurement, many combinations of the detailed parameters become mutually compensatory, and only a few parameter combinations remain relevant to describing the measurement results. Models exhibiting this type of behavior have been denoted sloppy . In the context of sloppy models, parameter combinations that make little or no difference to predicting or fitting the data are also called sloppy. The few remaining stiff parameter combinations capture the experimentally available information and serve as useful descriptors of the measurement. Sometimes, stiff parameter combinations correspond to familiar macroscopic descriptors and properties of systems . For example, a diffusion coefficient is a stiff parameter that can result from underlying molecular dynamics whose detailed description may be enormously complicated. As one of these sloppy models, multicomponent exponential relaxation can be approached through identification of stiff parameter combinations, determination of stiff parameter values that capture the information available from the data, and determination of the physical meaning of those stiff parameter combinations. Recognition of the sloppy parameter combinations is also valuable, allowing them to be appropriately handled.