Technological progress is leading to an increase of instrument sensitivity in the field of rotational spectroscopy. A direct consequence of such a progress is the increasing amount of data produced by instruments, for which the currently available analysis software is becoming limited and inadequate. In order improve data analysis performance, parallel computing techniques and distributed computing technologies like Cloud or High Performance Computing (HPC) can be exploited. Despite the availability of computer resources, neither Cloud computing nor HPC have been fully investigated for identifying unknown target spectra in rotational spectrum. This paper proposes the design and implementation of a Highly Scalable AUTOFIT (HS-AUTOFIT), an enhanced version of a fitting tool for broadband rotational spectra that is capable of exploiting the resources offered by multiple computing nodes. With respect to the old program version, the new one scales on multiple computing nodes thus guaranteeing higher accuracy of the fit function and consistent boost of execution time. The result of tests conducted in real Cloud and HPC environments demonstrate that HS-AUTOFIT is a viable solution for the analysis of huge amount of data in the addressed scientific field.