In the ever-evolving world of artificial intelligence, groundbreaking innovations are redefining the boundaries of what’s possible.

At the forefront of this transformative landscape is Vast Data, a data platform company that is changing the way the enterprise approaches AI infrastructure. In an exclusive interview, Jeff Denworth (pictured), co-founder of Vast Data, provided insights into the company’s vision and impact its platform is having on the industry.

“I think the separation between application and storage, whether you’re talking about unstructured data or database infrastructure, has always been an encumbrance to people that ultimately want to try to solve that first principles problem,” Denworth said. “Data has a ton of gravity as we all talk about, but if you can essentially attach code to your data … there’s something simpler here by the synthesis of data and code, and we need to work a lot over the next couple years to convince the market.”

Denworth spoke with theCUBE industry analysts Jeff Furrier and Dave Vellante at the Vast presents: Build Beyond event, during an exclusive broadcast on theCUBE, SiliconANGLE Media’s livestreaming studio. They discussed Vast’s vision and the impact its data platform is having on the industry. (* Disclosure below.)

Unveiling the Vast Data Platform: Simplifying AI infrastructure

Vast’s mission is to address the complexities of AI infrastructure and create a unified data platform that caters to the needs of data developers. The Vast platform eliminates the need for enterprises to struggle with piecing together various technologies to build deep learning pipelines, according to Denworth. The concept of a data platform, in the company’s vision, involves a combination of compute, data and storage layers wrapped into a seamless package.

Vast’s approach simplifies the transition from traditional business reporting systems to advanced deep learning projects. By bridging this gap, the company is making AI infrastructure simpler and more accessible to data developers.

“The way we look at it is, Vast has the fortune of working with some of the largest AI projects in the world. You watch the collection of technologies that people need to put together to build things like deep learning pipelines … there’s nothing in this space. So, we’re here to make it much simpler than it’s been in the past,” Denworth said.

Reimagining AI infrastructure: The impact on the storage industry

One of the most groundbreaking aspects of Vast’s platform is its impact on the storage industry. Denworth refers to this as the “Grand Unification Theory of AI Infrastructure.” By making flash storage as cost-efficient as disk storage, the platform achieves a single-tier infrastructure with exponentially faster data access. This breakthrough allows for a complete consolidation of the storage and database stacks, paving the way for unprecedented levels of operational efficiency.

The traditional approach of data partitioning in distributed systems often leads to transactional consistency issues and performance bottlenecks, according to Denworth. In contrast, Vast’s disaggregated, shared-everything architecture enables true parallelism, unleashing the full potential of AI at scale.

“The thinking is if you can make flash as cheap as disk, well then you just need one tier of infrastructure,” Denworth said. “Then we looked over in the database space and we said, well, if you can build a system that’s transactional and can handle streams … I’ve got this one data system in my environment. Let’s squash all the data, get data centers together, both edge and cloud and on-prem data centers. It’s a big simplification that we’re aiming at.”

Here’s the complete video interview, part of SiliconANGLE’s and theCUBE’s coverage of the Vast presents: Build Beyond event:

(* Disclosure: TheCUBE is a paid media partner for the “Vast presents: Build Beyond” event. Neither Vast Data Inc., the sponsor of theCUBE’s event coverage, nor other sponsors have editorial control over content on theCUBE or SiliconANGLE.)

Photo: SiliconANGLE