Summary: VAST Data raised $1 billion in a Series F at a valuation of $30 billion, more than triple the $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, over $500 million in committed ARR, and has positive free cash flow with revenue approximately tripling year over year. Key customers include xAI’s 200,000-GPU Colossus cluster and CoreWeave’s $1.17 billion deal.
VAST Data raised $1 billion in a Series F round at a valuation of $30 billion, more than triple 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 participation from Nvidia, Fidelity Management and Research Company and NEA. 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 eases liquidity pressure on long-time shareholders and reduces the urgency of an initial public offering. 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 draws attention not because a company will have raised $1 billion in 2026, a year in which record AI funding rounds have reshaped expectations for what venture capital looks like, but because VAST Data sells data infrastructure, the layer of the AI stack that sits between GPUs and models. It is not a foundation model company. It is not a cloud provider. It is the company that ensures that data reaches the processors fast enough to keep them busy. Nvidia CEO Jensen Huang recorded a personal endorsement at VAST’s Forward 2026 conference, stating that “with VAST Data, we are transforming AI infrastructure storage” and explaining that without VAST technology, even the fastest AI processors face severe data bottlenecks. When the company that makes GPUs tells you that 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 on a single platform. The core architecture, called DASE (Disaggregated and Shared Everything), was announced when the company came out of 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 tiers. For AI workloads, where training runs consume petabytes of data with sustained high performance, removing tiering eliminates a bottleneck that legacy storage systems were never designed to handle.
The platform has expanded far 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 AI pipelines in real-time, handling chunking, embedding, vectorization, and retrieval for generation, semantic search, and classification with augmented recall. VAST DataBase includes an integrated vector warehouse that the company says supports a scale of trillions of vectors with constant-time search. VAST CNode-X, an Nvidia-certified system, turns GPU servers into 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 argument is that VAST is not a storage company that added AI features. It’s a data platform built for AI from the ground up, and storage is just the foundation.
the numbers
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VAST Data has amassed more than $4 billion in cumulative reserves and reports more than $500 million in committed annual recurring revenue at the end of fiscal 2026. CTech, the technology publication of Israeli financial newspaper Calcalist, reports that total ARR, including uncommitted revenue, has reached $2 billion. Revenue has roughly tripled year over year. The company is generating over $100 million in cash per quarter and has positive free cash flow with a positive operating margin, unusual for a company with 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 last five to seven years.
Outstanding customer relationships illustrate 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 commercial deal worth $1.17 billion in November 2025, using VAST as the primary database for its Nvidia-accelerated computing cloud. Other customers include Pixar, which uses the platform for petabytes of assets rendered 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, founder and CEO of VAST, said the company “already supports AI environments spanning millions of GPUs around the world, operating at every layer of the AI stack.”
The data layer thesis.
The investment thesis behind a $30 billion valuation for a 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. Growing global investment in AIwhich the Stanford AI Index pegged at $285.9 billion in U.S. private AI capital in 2025 alone, has been overwhelmingly focused on computer science. 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 models, is increasingly recognized as the binding constraint on AI performance.
That’s why Nvidia is not only investing in VAST Data but 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 runs the models, eliminating the traditional separation between storage infrastructure and compute infrastructure. Nvidia-backed AI infrastructure companies They now span the entire spectrum, from cloud GPU providers to chip manufacturing and data platforms, and VAST’s role is to ensure data moves as fast as the silicon can process it.
AI Infrastructure Startup Valuations have increased considerably throughout the sector. FluidStack is in talks to raise $1 billion at a valuation of $18 billion. CoreWeave, VAST’s largest customer, was valued at $35 billion earlier this year. Enterprise AI Infrastructure Offerings such as Jane Street’s $6 billion cloud commitment to CoreWeave, with a $1 billion capital investment attached, illustrate that demand for AI infrastructure is expanding beyond hyperscalers into financial services, healthcare, and government. VAST’s position in the data layer of these environments, not the compute layer or the model layer, is what distinguishes the valuation argument from that of GPU cloud companies. If the computing 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 work.
The competitive landscape
VAST Data is not the only company building an AI-native data infrastructure. DDN and WEKA are the two most frequently cited competitors and both offer high-performance storage platforms optimized 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 deepening their integrations with Nvidia and repositioning their storage portfolios for AI. Pure Storage’s FlashBlade products compete directly with VAST in 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 tailored for AI, cannot deliver the sustained performance that Colossus-scale training runs require. The single-tier, flash-based architecture eliminates the data movement imposed by tiered systems, and the integrated database and computing capabilities mean that data transformation—the fragmentation, embedding, and vectorization that AI pipelines require—occurs within the platform rather than in a separate processing layer. Whether that architectural advantage lasts or whether 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 initial public offering in the second half of 2026 or later, according to The Information. The secondary structure of Series F suggests that the timeline is not imminent. VAST Data can afford to wait. It is a positive cash flow, triples income and is located at the center of technological development with the greatest use of capital from the Internet. The question is not whether the data layer matters. It’s about whether $30 billion is the right price for the company building it.
