The Standardized Precipitation Index (SPI), a drought index widely used for drought monitoring, is estimated based on the probability of deficit in precipitation at any time scale. It is a simple and flexible drought index as it requires only precipitation as input data and it can quantify the degree of dryness/wetness on multiple time scales to address different types of droughts. Availability of free remotely sensed precipitation data and long term reanalysis meteorological data makes the usage of SPI more convenient at various spatial and temporal scales. At the same time, processing such high-volume data requires a high magnitude of processing power and time. This paper introduces SPI Utility, an open-source, light-weight and fast graphical user interface to estimate SPI values at different spatial and temporal scales. It is developed in Python using Geospatial Data Abstraction Library (GDAL), Numpy, Scipy and PyQt4 libraries as a plugin into Quantum Geographic Information System (QGIS). It can generate SPI values for 1-, 3-, 4-, 6-, 9-, 12-, 24- and 36- month timescales for any specified month range. The developed SPI Utility also classifies the generated SPI values into user-defined drought classes to generate the count and probability of occurrence of the drought classes. It can handle long duration precipitation data in form of both raster files and point data files. cProfile profiling showed that the plugin could generate annual SPI values for 117 years for rasters having dimension 135 and 129 for any timescale in around 8 minutes in a system having moderate specifications. The tool can be used for drought monitoring in low performance systems and early drought warning systems.