Images from agile (viewing angle over 40°) and very high spatial resolution satellites (inferior to 1 m) can be useful for monitoring cliff faces, which is the best proxy to better understand coastal cliff dynamics. However, these images with a specific configuration are rarely used, partly because it is cumbersome to process them. Based on Pléiades images of the coastal cliff face along the coast of Normandy, with a high angle of incidence (up to 40°) and taken on multiple dates, the paper aims to identify i) the best open-source processing chain to reconstitute three-dimensional (3D) cliff faces by stereo restitution ii) the reasons behind its best performance and iii) the key parameters to change depending on the image datasets or processing chains so as to facilitate transposition. The Ames Stereo Pipeline® (ASP®) and MicMac® software programmes were tested using different parameters (matching algorithm, size of correlation window, etc.) for the 3D reconstructions. MicMac® provides the best performance using GeomImage (1–2 pixel matching) with a size of correlation window of 3 × 3 or 7 × 7 associated with a regularization parameter of 0.10. With these parameters, the point clouds of the cliff face have an average point density of 1.70 point m−2, a mean distance from Unmanned Aerial Vehicle (UAV) ground truth data of 0.04 m and a standard deviation of 1.72 m. With these characteristics, the threshold of rockfall detection using a multi-source comparison is assessed at 100 m3, which involves that the large majority of rockfalls (69%) around the study area could be detected by a diachronic approach. Considering the daily Pléiades revisiting time, this method offers a great opportunity to monitor erosion and to better understand coastal cliff dynamics.