With the multi-source nature of remote sensing scientific data, the automation of processing, and the widespread application, the evolution of methods such as PS-InSAR, SBAS, and SqueeSAR, as well as the maturity of processing tools like GAMMA and StaMPS, have led many scholars and researchers to face concerns regarding the selection of multi-source data preprocessing, platform compatibility, and result accuracy evaluation during the data processing process. This paper selects three platforms, namely AI Earth, SARvey, and SARscape (as the reference benchmark), and conducts an applicability analysis of the platforms from multiple dimensions, including processing accuracy, operation efficiency, and functional integrity. We found that: SARscape has comprehensive functions and a high degree of visualization in the processing process; AI Earth achieves efficient computing relying on a cloud-based architecture, but there are limitations in terms of spatial and temporal scope; the open-source platform SARvey performs excellently in extracting time-series information, but it needs to rely on external ISCE for preprocessing. We propose a data processing collaborative framework that integrates the advantages of cloud-based processing and open-source analysis, aiming to provide technical references for the processing of multi-source remote sensing data and platform selection in different application scenarios.