Summary: VAST Data raised $1 billion in Series F at a $30 billion valuation, with participation from Drive Capital and Access Industries and Nvidia, Fidelity and NEA, more than tripling to $9.1 billion. More than $500 million is Tier 2 capital. The company reports $4 billion in gross bookings, $500 million-plus in ARR, and is free cash flow positive, with revenue nearly tripling annually. Key customers include xAI’s 200,000-GPU Colossus cluster and CoreWeave’s $1.17 billion contract.
VAST Data raised $1 billion in Series F at a $30 billion valuation, more than tripling the $9.1 billion it was valued at in Series E at the end of 2023. Drive Capital and Access Industries co-led the round, with participation from Nvidia, Fidelity Management and Research Company and NEA. More than $500 million of the total is tier 1 capital, meaning it goes to early investors and employees who sell shares rather than to the company’s coffers, a structure that reduces liquidity pressure on long-term shareholders and reduces the urgency of an IPO. The round makes VAST Data the most valuable private technology company founded in Israel since Google bought Wiz for $32 billion in March.
The valuation is amazing, a company has raised one billion dollars in 2026. Record AI funding rounds have reshaped expectations of what venture capital looks like, but because VAST Data sells the data infrastructure, the layer of the AI stack that sits between GPUs and models. This is not a foundational model company. This is not a cloud provider. It’s the company that ensures data arrives quickly enough to keep processors busy. Nvidia CEO Jensen Huang gave a personal endorsement of VAST at the Forward 2026 conference, saying, “With VAST Data, we’re transforming AI infrastructure maintenance,” explaining that without VAST technology, even the fastest AI processors face serious data bottlenecks. When the company that makes GPUs tells you that GPUs are useless without a specific data platform, investors listen.
What VAST Data actually does
VAST Data provides what it calls an AI operating system that combines storage, database and computing into a single platform. The underlying architecture, called DASE (Disaggregated and Shared Everything), was announced when the company came out of stealth in February 2019. It is tier one and tier one, eliminating the traditional memory hierarchy where data moves between fast, expensive tiers and slow, inexpensive tiers. De-tiering for AI workloads where training consumes petabytes of data with sustained high throughput removes the bottleneck that legacy storage systems were never designed to handle.
The platform has expanded beyond memory. VAST DataSpace provides a globally distributed namespace across on-premise, cloud and off-premise locations, scaling to exabytes and trillions of files. VAST InsightEngine automates real-time AI pipelines, handling segmentation, embedding, vectorization, and search for search augmentation, semantic search, and classification. VAST DataBase includes an integrated vector store that the company claims supports a trillion vector scales with constant retrieval. VAST CNode-X, an Nvidia-certified system, makes GPU servers the first-class infrastructure components within the platform with a fully CUDA-accelerated version of the operating system designed to run directly on Nvidia-powered servers. The thing is, VAST is not a storage company adding AI features. It’s a data platform built from the ground up for AI, and storage is just the foundation.
Numbers
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VAST Data has raised more than $4 billion in cumulative bookings and reports annual recurring revenue of more than $500 million by the end of fiscal 2026. CTech, the technology publication of Israeli financial newspaper Calcalist, reports that total ARR, including uncommitted revenue, reached $2 billion. The income has almost tripled every year. The company generates more than $100 million in cash per quarter and positive free cash flow with a positive operating margin, which is unusual for a company at this growth rate. Among Fortune 1000 companies, the customer base quadrupled, and the top 100 new customers spent an average of more than $1.2 million. Contracts typically last five to seven years.
It shows the scale of customer relations. VAST Data powers the data platform behind xAI’s Colossus supercomputing cluster, which has more than 200,000 Nvidia GPUs, where VAST reduces total cost of ownership by 50%. CoreWeave signed a $1.17 billion commercial deal in November 2025 to use VAST as the core database for Nvidia’s accelerated computing cloud. Other customers include Pixar, NASA, the US Department of Energy, Boston Children’s Hospital, Booking Holdings and several of the world’s largest banks, which use the platform for petabytes of assets served as AI training data. VAST founder and CEO Renen Hallak said the company “already supports AI environments spanning millions of GPUs globally operating at every layer of the AI stack.”
Data layer thesis
The investment thesis behind the $30 billion valuation for the data infrastructure company is based on a structural argument about how the AI stack works. The industry has spent three years and hundreds of billions of dollars on GPUs. The rise of global AI investmentsThe Stanford AI Index projects $285.9 billion in US private AI capital in 2025 alone, the vast majority of which is focused on computing. But a GPU waiting for data is a non-exercising GPU. The data layer, the infrastructure that stores, indexes, moves and transforms the data that feeds the models, is increasingly recognized as a binding constraint on AI performance.
That’s why Nvidia is not only investing in VAST Data, but also actively integrating its technology. The CUDA-accelerated operating system and CNode-X certification mean that the VAST platform is designed to run on the same Nvidia hardware that drives the models, eliminating the traditional distinction between memory infrastructure and compute infrastructure. AI infrastructure companies supported by Nvidia Now, GPUs span the entire stack, from cloud providers to chip manufacturing and data platforms, and VAST’s role is to keep data moving as fast as the silicon can process it.
Initial assessments of AI infrastructure has risen sharply in the sector. FluidStack is in talks to raise $1 billion at an $18 billion valuation. VAST’s largest customer, CoreWeave, was valued at $35 billion earlier this year. Enterprise AI infrastructure deals An additional $1 billion in equity investment, like Jane Street’s $6 billion cloud commitment to CoreWeave, shows that demand for AI infrastructure is expanding beyond hyperscalers to financial services, healthcare and government. What differentiates the pricing argument from GPU cloud companies is VAST’s position in the data layer of these environments, not the model layer, but the compute layer. If the compute layer is the engine, VAST is the fuel line. A $30 billion fuel line is expensive. The argument is that the engine doesn’t work without it.
A competitive landscape
VAST Data is not the only company building an AI-native data infrastructure. DDN and WEKA are the two most cited competitors, both offering high-performance storage platforms optimized for machine learning workloads. Hammerspace provides a global data orchestration layer. Incumbent companies Dell, HPE, Hitachi Vantara, IBM, NetApp and Pure Storage (recently rebranded as Everpure) are all deepening Nvidia integrations and repositioning their storage portfolios for AI. Pure Storage’s FlashBlade products compete directly with VAST in terms of performance. NetApp has expanded its AI storage services. They all 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 optimized for AI cannot provide the sustained throughput required by Colossus-scale training. A tier-first architecture eliminates the data movement that tiered systems impose, and integrated database and computing capabilities mean that the data transformation, decomposition, positioning, and vectorization required by AI pipelines occur within the platform, rather than at a separate processing layer. Whether that architectural advantage is sustainable, or whether officials can close the gap, will determine whether the $30 billion valuation looks advanced or excessive in three years.
Hallak has told employees and bankers that the company is considering an IPO in the second half of 2026 or later. The secondary-heavy structure of the F series suggests that the timeline is not imminent. VAST Data can wait. It’s cash-flow positive, triples revenue, and sits at the heart of the most capital-intensive technology structure since the internet. The question is not whether the data layer is important. Whether $30 billion is the right price for the company that built it.
