VAST Data has signed a $1.17bn worth commercial agreement with CoreWeave, furthering their collaboration on building large-scale AI infrastructure.
With this agreement, CoreWeave will continue to use the VAST AI Operating System (AI OS) as the core data platform underpinning its AI-focused cloud services.
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The partnership enables CoreWeave to roll out VAST Data’s architecture across all its data centres, supporting workloads that require high-performance computing and instant access to extensive datasets.
Both training and inference operations for AI models will operate on this foundation, which CoreWeave asserts removes concerns over scaling or reliability.
CoreWeave co-founder and chief strategy officer Brian Venturo said: “The VAST AI Operating System underpins key aspects of how we design and deliver our AI cloud.
“This partnership enables us to deliver AI infrastructure that is the most performant, scalable, and cost-efficient in the market, while reinforcing the trust and reliability of a data platform that our customers depend on for their most demanding workloads.”
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By GlobalDataJoint development between the two companies will provide data services for shared customers, spanning the application stack from storage to advanced data pipeline optimisation.
This is intended to address the requirements of teams building and deploying models at scale.
By integrating CoreWeave’s GPU-accelerated infrastructure with VAST Data’s operating system, the companies aim to support continuous model training, real-time inference, and data processing for industries where these capabilities are essential.
VAST Data founder and CEO Renen Hallak said: “At VAST, we are building the data foundation for the most ambitious AI initiatives in the world.
“Our deep integration with CoreWeave is the result of a long-term commitment to working side by side at both the business and technical level.”
In October 2025, CoreWeave announced an agreement to acquire Monolith AI, a company specialising in AI and machine learning solutions for physics and engineering challenges.
