Centersquare has acquired 10 data centers across the United States and Canada, expanding its portfolio to 80 facilities and significantly increasing its capacity to support high density computing workloads.
“This package of acquisitions represents more than just volume growth, it reflects the growing strength and gaining momentum of our platform,” said Spencer Mullee, CEO of Centersquare. “By adding capacity in strategic markets, we are positioning Centersquare to capture the surging demand for trusted, high-performance infrastructure. Our customers from enterprise to scale are looking for partners who can grow with them, and we are delivering the reliable power, connectivity, and engineered environments they need to accelerate innovation.”
These transactions, with enterprise value of $1 billion, are self-funded by Centersquare using cash on hand and include the purchase of two data centers in Boston and Minneapolis that the company had been operating under long-term lease agreements, along with eight additional colocation facilities in Dallas, Tulsa, Nashville, Raleigh, Toronto, and Montreal. Together, these sites bring substantial power and space to Centersquare’s platform, house a robust base of enterprise and hyperscale customers, and provide abundant room for future expansion.
“Workloads are shifting — we’re seeing enterprises move off-premises, reconsider public cloud strategies, and embrace AI-driven architectures that demand radically higher power densities,” said Udhay Mathialagan, CEO of Brookfield Global Data Centers and Chair, Centersquare. “Over the last two years, Centersquare has continued to grow its customer and revenue base and developed a strong cost-efficient operating platform. These factors position the company well to make smart, value-accretive acquisitions such as these with the benefit of high confidence levels on revenues and costs.”
As AI adoption accelerates, data center operators face unprecedented demands for scalable, energy-efficient, and resilient infrastructure. Centersquare has been investing heavily in solutions that meet these requirements, with a focus on supporting GPU-intensive workloads that drive generative AI, machine learning, and advanced analytics.
