Vast Data raises $1 billion, triples valuation to $30 billion as AI infrastructure demand surges

Vast Data Secures $1 Billion in Series F Funding Round

Vast Data has closed a $1 billion Series F funding round that values the Nvidia-backed AI company at $30 billion, more than tripling the $9.1 billion valuation it achieved when it last raised funds in 2023 1

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. The round was co-led by Drive Capital and Access Industries, with participation from existing investors including Nvidia, Fidelity Management & Research Company, and NEA 2

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. The financing includes both primary and secondary capital, providing liquidity for early investors and employees while giving the company flexibility on whether and when to pursue an initial public offering 1

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Founded in 2016, Vast Data develops software for AI applications and storage technology specifically designed for large AI data centers, enabling efficient data movement between GPUs made by companies like Nvidia 2

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. The company's founder and CEO, Renen Hallak, said the size of the round was driven by investor interest rather than a predefined capital target, noting that the company has been cash-flow positive for several years and does not require external capital to fund operations 4

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AI Infrastructure Demand Drives Record Growth

The capital injection comes as AI infrastructure demand accelerates globally. Record funding has already been raised this year by AI companies worldwide, with investors committing $280.5 billion, according to Dealroom 3

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. Vast Data is profitable on an operating basis and has generated $4 billion in bookings since its founding a decade ago, most of that in the past year 1

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. The company exited the previous fiscal year with more than $500 million in committed annual recurring revenue 3

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Chris Olsen, co-founder and partner at Drive Capital, stated that "the scale and speed of AI adoption are creating a new class of infrastructure company," with Vast Data emerging as the clear leader in this category 2

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. The company's platform is already deployed in production environments supporting large-scale AI workloads, including systems with thousands of GPUs supporting millions of GPUs in production 4

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From Storage to AI Operating System

Vast Data has evolved from its origins as a storage company into what Renen Hallak describes as an "AI operating system" provider 5

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. The company's platform now integrates data management, analytics, and compute orchestration through what it calls a "disaggregated shared-everything architecture" 4

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. This evolution reflects the market's shift from model training to inference and real-time decision-making, where data access, latency, and resilience become critical factors.

The company sells software and appliances designed to replace traditional storage and database products, better enabling customers to access information for training and running AI programs 1

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. Vast Data counts JPMorgan Chase, Elon Musk's xAI, CoreWeave, Mistral, the U.S. Air Force, and Cursor among its customers 1

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Expansion Plans and IPO Readiness

Vast Data is considering using the new capital for acquisitions and investments in other firms in the AI ecosystem, according to Hallak 1

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. The company is also partnering with funds in the United Arab Emirates and Singapore to help expand its presence in those regions, with additional global expansion planned for Latin America, India, and China 1

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The company hired Chief Financial Officer Amy Shapero about 18 months ago partly to prepare for a public share sale 1

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. Hallak said the company expects to be ready for an initial public offering by the end of this year, though the timing remains flexible. "We should be ready by the end of this year and then we get to decide if and when to pull the trigger," he stated, adding that "what really matters to me is how do we grow faster" 1

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Vast Data is also extending its platform to support agent-based AI systems, which require coordination across distributed environments and stricter controls over how data is accessed and shared 4

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. The company's platform already supports clusters that are multi-exabyte in size with hundreds of thousands of GPUs, demonstrating the scale required for production AI deployments 4

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. As enterprises move from experimentation to production-scale AI workloads, Hallak predicts that "five years from now, AI is many orders of magnitude bigger than it is now" 4

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