Ambient ionization-based techniques hold great potential for rapid point-of-care applicable metabolic fingerprinting of tissue and fluids. Hereby, feces represents a unique biospecimen as it integrates the complex interactions between the diet, gut microbiome and host, and is therefore ideally suited to study the involvement of the diet-gut microbiome axis in metabolic diseases and their treatments at a molecular level. We present a new method for rapid (<10 s) metabolic fingerprinting of feces, i.e. laser-assisted rapid evaporative ionization mass spectrometry (LA-REIMS) with an Nd:YAG laser (2940 nm) and quadrupole Time-of-Flight mass spectrometer as main components. The LA-REIMS method was implemented on mimicked crude feces samples from individuals that were assigned a state of type 2 diabetes or euglycaemia. Based on the generated fingerprints, enclosing 4923 feature ions, significant segregation according to disease classification was achieved through orthogonal partial least squares discriminant analysis (Q2(Y) of 0.734 and p-value of 1.93e-17) and endorsed by a general classification accuracy of 90.5%. A comparison between the discriminative performance of the novel LA-REIMS and our established ultra-high performance liquid-chromatography high-resolution MS (UHPLC-HRMS) metabolomics and lipidomics methodologies for fingerprinting of stool was performed. Based on the supervised modelling results upon UHPLC-HRMS (Q2(Y) ≥ 0.655 and p-value ≤ 4.11 e-5), equivalent or better discriminative performance of LA-REIMS fingerprinting was concluded. Eventually, comprehensive UHPLC-HRMS was employed to assess metabolic alterations as observed for the defined classes, whereby metformin treatment of the type 2 diabetes patients was considered a relevant study factor to acquire new mechanistic insights. More specifically, ten metabolization products of metformin were identified, with (hydroxylated) triazepinone and metformin-cholesterol reported for the first time in vivo.In conclusion, LA-REIMS was established as an expedient strategy for rapid metabolic fingerprinting of feces, whereby potential implementations may relate, but are not limited to differential diagnosis and treatment efficacy evaluation of metabolic diseases. Yet, LC-HRMS remains essential for in-depth biological interpretation.