OGC spatial data services, particularly the Web Feature Service (WFS), are widely used and gaining popularity due to factors like open data policies and the implementation of spatial data infrastructures by governments and institutions. This study presents a detailed performance evaluation of Web Feature Service (WFS) consumption through various processes such as connection, data capture, storage, and retrieval using Python and the OWSLib library. Additionally, it conducts a comparative performance analysis between CPython and Cython to determine efficiency differences in real execution scenarios. The experiments involved three international WFS services and implemented key algorithms in three modes: CPython, pure Cython, and Cython with static typing. The findings reinforce previous research highlighting Cython’s ability to optimize computational performance, particularly when static variable typing is applied. Significant execution time improvements were observed in specific tasks involving spatial data handling. These results have practical implications for geospatial application developers and can serve as a reference for improving high-performance spatial data workflows.