Mavria, which aims to build modular data centers that can be shipped by trucks, raised $130M across pre-seed and seed rounds at a $1B valuation led by Paradigm
First reported by WSJ ·
Factory-built, modular data centers can now be shipped and plugged into existing local power lines to bypass grid delays.
Mavria, a new modular data center firm founded by former US Department of Energy staffers, has raised $130 million in pre-seed and seed funding, achieving a $1 billion valuation. The company, led by CEO Adam Blake, aims to revolutionize data center construction with its factory-manufactured product called Terrain. This modular platform replaces traditional on-site building with repeatable manufacturing, integrating power distribution, liquid cooling, and automation controls into 5MW units. Mavria's strategy is to bypass grid connection delays by directly tapping into local distribution lines, utilizing excess capacity. The company is currently producing units in Houston, Texas, with testing scheduled for early 2027 and scaled production expected by mid-2027. Mavria is actively hiring across engineering, manufacturing, and operations to support its expansion.
Mavria's $130 million funding round and $1 billion valuation signal a significant market appetite for prefabricated data center solutions, particularly those addressing grid capacity and construction timelines. The company's strategy of leveraging excess local power capacity and its factory-to-deployment model could offer a faster and more predictable path to new data center capacity, directly challenging traditional build-to-suit approaches.
This development is poised to affect cloud providers and large enterprises seeking to rapidly scale their AI and compute infrastructure without being bottlenecked by utility infrastructure or lengthy construction schedules. The success of Mavria's approach may spur increased investment in other modular and off-site construction firms, potentially reshaping the competitive landscape for data center development and operations.
AI-written summary. May contain errors.