Stochastic simulation of enzymatic kinetics is uncommon, but it can bring more insight to the results obtained with the usual deterministic simulation based on the integration of differential equations. This work covers the stochastic simulation of mass action and Michaelis-Menten kinetic models applied to generic examples of four enzymatic kinetic cases, with and without inhibition. For each case the deterministic model was translated to a stochastic formulation based on numbers of molecules instead of concentrations. The simulations were done applying the Gillespie algorithm, and the uncertainty of the results was estimated with the standard deviation, calculated from many realizations (repetitions) of the stochastic simulations for each case. The uncertainty of the concentration results is related with the number of molecules in the initial simulation setting. It was also found that a minimum number of realizations is enough to estimate the standard deviation of the results. However, due to the nature of the stochastic method the uncertainty of its results varies through the simulation. In sum, the uncertainty of enzyme kinetics can be estimated \emph{in-silico} with stochastic simulation.