The World Meteorological Organizations' International Cloud Atlas recognizes 10 basic cloud genera for classifying clouds. Many of these have been used for over 200 years and are based on cloud appearance and base altitude as seen from surface. Over the satellite era, several missions and programs provide public products that classify clouds into these cloud genera. Here, we provide the first comparison of three such satellite climatologies of cloud genera with surface observations. Specifically, we analyse 10 years (2007–2016) of CloudSat, 4 years (2007–2010) of joint CloudSat‐CALIPSO, 13 years (2000–2012) of ISCCP‐H, and 11 years (1998–2008) of the EECRA data between 50°N and 50°S for eight cloud genera. Averaged over this latitude range, the total cloud amounts for these datasets range from 0.56 to 0.65, with Cumulus (Cu) ranging from 0.06 to 0.14; Stratus (St) from 0.14 to 0.38; Altostratus (As) from 0.05 to 0.13; Altocumulus (Ac) from 0.07 to 0.17; Nimbostratus (Ns) from 0.03 to 0.06; Cirrus (Ci) from 0.1 to 0.19; and Deep‐convective (Dc) from 0.01 to 0.04. The largest disagreement among the sensors is observed for Dc cloud with the coefficient of variation of 44%. On the other hand, the cloud datasets show the best agreement for Ci cloud with the coefficient of variation of 24.1%. Regionally, however, the level of agreement and disagreement can vary drastically. For example, in Indian summer monsoon region (ISM 60°–90°E, 10°–30°N) Ci cloud shows a variation of 28%, whereas the Dc cloud shows 16% variation, which is the opposite of their near‐global feature. The observed discrepancies in cloud genera are discussed in terms of observing characteristics, including instrument, methods, and sampling. Greater effort is still required to reduce discrepancies among these datasets, and the assessment provided here can act as a guide for their use in climate studies. Clouds affect the global energy and the water cycle, the magnitude of which depends on the individual cloud types. Here, we present the first comparative assessment of the near‐global view of individual cloud types from four state‐of‐the‐art satellite and in situ datasets. The discrepancy of cloud fraction for different cloud genera and regions can be attributed to the sensors' ability in identifying specific cloud types.