This study focuses on comparing different kinetic growth models and the use of neural networks in the batch cultivation of Streptomyces peucetius var. caesius producing epsilon-rhodomycinone. Contois, Monod and Teissier microbial growth models were used as well as the logistic growth modeling approach, which was found best in the simulations of growth and glucose consumption in the batch growth phase. The lag phase was included in the kinetic model with a CO2 trigger and a delay factor. Substrate consumption and product formation were included as Luedeking-Piret and logistic type equations, respectively. Biomass formation was modeled successfully with a 6-8-2 network, and the network was capable of biomass prediction with an R2-value of 0.983. Epsilon-rhodomycinone production was successfully modeled with a recursive 8-3-1 network capable of epsilon-rhodomycinone prediction with an R2-value of 0.903. The predictive power of the neural networks was superior to the kinetic models, which could not be used in predictive modeling of arbitrary batch cultivations.