AI training clusters now pose significant cooling and resource challenges for data centers. Traditional air-cooled designs struggle with intense heat, straining electricity and water infrastructure. Evaporative cooling, while efficient, uses large water volumes, creating tension with local resource constraints and tightening regulations. Direct evaporative systems cool air by evaporating water, while indirect systems use a heat exchanger to keep process air separate. Both leverage water's latent heat of vaporization for effective cooling, saving energy compared to air-only systems. However, this energy saving shifts the burden to water resources, requiring careful trade-offs in water-stressed regions. This necessitates integrated resource analysis in site selection, considering both energy and water availability. Hybrid evaporative cooling systems, though having higher upfront capital costs, offer sustained performance in low-humidity environments. These hybrid designs integrate dry coolers with limited evaporative stages, controlled to optimize water and energy use, crucial for sovereign cloud compliance. Procurement teams should map facility locations against climate data and water-stress indices, model lifecycle costs, and ensure sustainability and resilience align with corporate goals. Global Cloud Data infrastructure services offer integrated solutions to address these complex cooling and infrastructure challenges.