Enterprises create a lot of data. And while that can be helpful for giving AI models richer context, using it can gum up the works.
It’s a problem that Vast Data aims to solve. Vast offers an AI operating system that consolidates enterprise data with the systems that need it, unifying data, systems and automation in one central place, allowing agents to reason about and act on a business’s context in real time.
And on Wednesday, Vast announced that it raised $1 billion in Series F funding, more than tripling its valuation to $30 billion. The round drew in investors such as Nvidia and Fidelity, reflecting growing demand to simplify the AI infrastructure stack as AI scales.
Vast’s system is built on what Renen Hallak, founder and CEO of Vast, calls the DASE architecture, or “Disaggregated Shared Everything,” a framework built by the company. To put it simply, Vast’s system operates data storage and AI systems in parallel, connecting them with ultrafast links to reduce latency. Any server within the system can access the central well of data, making it easier to scale the AI systems. The result? Enterprises no longer need to choose between scale and simplicity, or performance and cost.
Hallak told The Deep View that the idea came from improvements to neural networks at Google DeepMind in 2014. “The natural question in our minds was, what if we can give it even faster access to even more information?” Hallak said. “Will we get closer and closer to the human brain, and will we eventually surpass what the human brain can do?”
Vast has grown rapidly over the past year, tripling its staff and generating more than $100 million in quarterly revenue, Hallak told me. The company counts Mistral, CoreWeave, JPMorgan Chase, Microsoft and Google Cloud as customers.
“We focus on the most data-intensive organizations on the planet, and that's because they are the ones that need this level of scale,” said Hallak. “As we progressed through time, it went from being that small niche that we started from, to generative AI companies, all of the large language model builders and the frontier model builders.”
However, Vast was around long before the AI boom, Hallak told me. The company was founded a decade ago, putting out its first product in 2019, at a time when a “very small niche” needed it, such as researchers in autonomous systems, medicine or quant trading. Now, as AI models and agentic systems produce more data than ever, the tables have completely turned.
“It's called generative AI for a reason,” said Hallak. “As they generate text and code and pictures and video, then all of that data needs a place to live.”
Our Deeper View
As agents scale, they generate more data than ever, and the problems that Hallak and Vast aim to solve with the architecture are poised to grow. However, the market faces other constraints as the data well goes deeper, including memory and storage bottlenecks, Hallak told me. So while innovation can happen at all layers of the stack, a bottleneck in one layer can have a domino effect on the rest. In short, until shortages on the components themselves are eased, every part of the market is going to feel the squeeze.