Discussing the VAST Data Solution for AI

In this discussion, Keith Townsend interviewed John Mao and Neeloy Bhattacharyya from VAST Data. They discuss the company’s recent growth, including closing a funding round that values the company at $9.1 billion, due in part to significant sales and growth in the data storage market. To start, Keith asks about a recent report that VAST Data has achieved a 6% share of the data center flash storage market, which would be notable for an independent software data platform company.

The conversation shifts to VAST Data’s role in AI, noting that about 60% of their business is used for AI and high-performance computing (HPC) workloads. VAST Data has been involved in AI before it became a trending topic and has been working with large customers and AI service providers. The discussion then moves on to the unique aspects of the VAST platform that make it suitable for AI workloads. They talk about the company’s vision and strategy, which extends beyond traditional storage to include capabilities that address the entire AI data pipeline. VAST Data’s global namespace, which is right-consistent and allows for distributed inference and model serving, is a key feature that facilitates the AI pipeline by providing a common dataset for different locations to access and work from.

They also discuss VAST’s multi-protocol platform and its disaggregated shared-everything architecture, which allows for intelligent data movement based on actual workload needs rather than pre-staging data. Keith asks about how VAST helps with the data gravity problem and the challenges of moving compute closer to the data. Neeloy explains that VAST’s architecture, including its highly scalable metadata layer, allows for a better understanding of data access patterns and more intelligent pre-staging of data.

Finally, they touch upon VAST’s DataBase, which helps with the data preparation phase by assigning structure to unstructured data and accelerating ETL tools and query engines. This reduces the time necessary for data preparation, which is a significant part of the AI project lifecycle.