VAST Data’s $30 billion valuation is a bet that the data layer is the real bottleneck in AI

Summary: VAST Data raised $1 billion in a Series F at a $30 billion valuation, more than tripling from $9.1 billion, with Drive Capital and Access Industries co-leading and Nvidia, Fidelity, and NEA participating. More than $500 million is secondary capital. The company reports $4 billion in cumulative bookings, $500 million-plus in committed ARR, and is free cash flow positive with revenue roughly tripling year over year. Key customers include xAI’s 200,000-GPU Colossus cluster and CoreWeave’s $1.17 billion agreement.

VAST Data raised $1 billion in a Series F round at a $30 billion valuation, more than tripling the $9.1 billion it was valued at in its Series E in late 2023. Drive Capital and Access Industries co-led the round, with Nvidia, Fidelity Management and Research Company, and NEA participating. More than $500 million of the total is secondary capital, meaning it goes to early investors and employees selling shares rather than into the company’s treasury, a structure that relieves liquidity pressure on long-tenured shareholders and reduces the urgency of an IPO. The round makes VAST Data the most valuable private technology company founded in Israel, following Google’s $32 billion acquisition of Wiz in March.

The valuation is striking not because a company raised a billion dollars in 2026, a year in which record AI funding rounds have reshaped expectations of what venture-scale capital looks like, but because VAST Data sells data infrastructure, the layer of the AI stack that sits between the GPUs and the models. It is not a foundation model company. It is not a cloud provider. It is the company that ensures the data reaches the processors fast enough to keep them busy. Jensen Huang, Nvidia’s chief executive, recorded a personal endorsement at VAST’s Forward 2026 conference, stating that “with VAST Data, we’re transforming the storage of AI infrastructure” and explaining that without VAST’s technology, even the fastest AI processors face severe data bottlenecks. When the company that makes the GPUs tells you the GPUs are useless without a particular data platform, investors listen.

What VAST Data actually does

VAST Data provides what it calls an AI operating system that unifies storage, database, and compute into a single platform. The core architecture, called DASE (Disaggregated and Shared Everything), was announced when the company emerged from stealth in February 2019. It is flash-first and single-tier, eliminating the traditional storage hierarchy in which data moves between fast, expensive tiers and slow, cheap ones. For AI workloads, where training runs consume petabytes of data at sustained high throughput, the elimination of tiering removes a bottleneck that legacy storage systems were never designed to handle.

The platform has expanded well beyond storage. VAST DataSpace provides a globally distributed namespace across on-premises, cloud, and edge locations, scaling to exabytes and trillions of files. VAST InsightEngine automates real-time AI pipelines, handling chunking, embedding, vectorisation, and retrieval for retrieval-augmented generation, semantic search, and classification. VAST DataBase includes an integrated vector store that the company claims supports trillion-vector scale with constant-time search. VAST CNode-X, an Nvidia-certified system, makes GPU servers first-class infrastructure components inside the platform, with a fully CUDA-accelerated version of the operating system designed to run directly on Nvidia-powered servers. The pitch is that VAST is not a storage company that added AI features. It is a data platform that was built for AI from the beginning, and the storage is just the foundation.

The numbers

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VAST Data has accumulated more than $4 billion in cumulative bookings and reports more than $500 million in committed annual recurring revenue as of the end of fiscal year 2026. CTech, the technology publication of the Israeli financial newspaper Calcalist, reports that total ARR including non-committed revenue has reached $2 billion. Revenue has been roughly tripling year over year. The company is generating more than $100 million in cash per quarter and is free cash flow positive with a positive operating margin, unusual for a company at this growth rate. The customer base has quadrupled among Fortune 1000 companies, with the top 100 new customers spending more than $1.2 million on average. Contracts typically run five to seven years.

The marquee customer relationships illustrate the scale. VAST Data powers the data platform behind xAI’s Colossus supercomputing cluster, a facility with more than 200,000 Nvidia GPUs where VAST says it reduced total cost of ownership by 50%. CoreWeave signed a $1.17 billion commercial agreement in November 2025, using VAST as the primary data foundation for its Nvidia-accelerated computing cloud. Other customers include Pixar, which uses the platform for petabytes of rendered assets as AI training data, NASA, the US Department of Energy, Boston Children’s Hospital, Booking Holdings, and several of the world’s largest banks. Renen Hallak, VAST’s founder and chief executive, said the company is “already supporting AI environments spanning millions of GPUs globally, operating across every layer of the AI stack.”

The data layer thesis

The investment thesis behind a $30 billion valuation for a data infrastructure company rests on a structural argument about how the AI stack works. The industry has spent three years and hundreds of billions of dollars on GPUs. Surging global AI investment, which the Stanford AI Index pegged at $285.9 billion in US private AI capital in 2025 alone, has been concentrated overwhelmingly on compute. But a GPU that is waiting for data is a GPU that is not training. The data layer, the infrastructure that stores, indexes, moves, and transforms the data that feeds the models, is increasingly recognised as the binding constraint on AI performance.

This is why Nvidia is not just investing in VAST Data but actively integrating its technology. The CUDA-accelerated operating system and CNode-X certification mean that VAST’s platform is designed to run on the same Nvidia hardware that runs the models, eliminating the traditional separation between storage infrastructure and compute infrastructure. Nvidia-backed AI infrastructure companies now span the entire stack, from GPU cloud providers to chip fabrication to data platforms, and VAST’s role is to ensure that the data moves as fast as the silicon can process it.

AI infrastructure startup valuations have been climbing sharply across the sector. FluidStack is in talks to raise $1 billion at an $18 billion valuation. CoreWeave, VAST’s largest customer, was valued at $35 billion earlier this year. Enterprise AI infrastructure deals like Jane Street’s $6 billion cloud commitment to CoreWeave, with a $1 billion equity investment attached, illustrate that demand for AI infrastructure is broadening beyond the hyperscalers into financial services, healthcare, and government. VAST’s position at the data layer of these environments, not the compute layer and not the model layer, is what makes the valuation argument distinct from the GPU cloud companies. If the compute layer is the engine, VAST is the fuel line. A $30 billion fuel line is expensive. The argument is that without it, the engine does not run.

The competitive landscape

VAST Data is not the only company building AI-native data infrastructure. DDN and WEKA are the two most frequently cited competitors, both offering high-performance storage platforms optimised for machine learning workloads. Hammerspace provides a global data orchestration layer. The incumbents, Dell, HPE, Hitachi Vantara, IBM, NetApp, and Pure Storage (recently rebranded as Everpure), are all deepening their Nvidia integrations and repositioning their storage portfolios for AI. Pure Storage’s FlashBlade products compete directly with VAST on performance. NetApp has expanded its AI storage services. All of them have larger installed bases and longer customer relationships than VAST.

VAST’s argument is that legacy storage architectures, designed for databases and file servers and retrofitted for AI, cannot deliver the sustained throughput that training runs at the scale of Colossus require. The single-tier, flash-first architecture eliminates the data movement that tiered systems impose, and the integrated database and compute capabilities mean that data transformation, the chunking, embedding, and vectorisation that AI pipelines require, happens within the platform rather than in a separate processing layer. Whether that architectural advantage is durable or whether the incumbents can close the gap will determine whether a $30 billion valuation looks prescient or excessive in three years.

Hallak has told employees and bankers that the company has considered an IPO in the second half of 2026 or later, according to The Information. The secondary-heavy structure of the Series F suggests that timeline is not imminent. VAST Data can afford to wait. It is cash-flow positive, tripling revenue, and sitting at the centre of the most capital-intensive technology buildout since the internet. The question is not whether the data layer matters. It is whether $30 billion is the right price for the company that is building it.