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An AI datacenter is a facility packed with accelerators, high-speed networking, and advanced cooling, built to train and serve AI models at scale.
AI datacenters differ from traditional ones in degree until degree becomes kind: rack power densities many times higher than conventional facilities, liquid cooling as standard, and network fabrics that bind tens of thousands of accelerators into single training clusters. Power is the defining constraint — campuses are now planned in hundreds of megawatts and gigawatts, and siting follows electricity supply.
The buildout is one of the largest capital deployments in technology history, with frontier labs, hyperscalers, and specialist neocloud operators committing hundreds of billions of dollars to capacity, alongside multi-gigawatt flagship projects announced by lab-hyperscaler alliances.
That capital sits underneath every API call: depreciation on chips and buildings plus energy per token forms the cost base that per-million-token prices must recover. Capacity gluts and shortages at the datacenter layer show up, with a lag, as pricing and rate-limit changes at the API layer.
Last revised 2026-07-05 · All glossary terms → · Live AI model pricing →