Operationalizing AI at Scale with VAST Data

John Mao of VAST Data introduces the company, highlighting its focus on AI workloads and its growth since its founding in 2016. VAST Data has achieved significant success, including raising a Series C round at a $9.1 billion valuation, doubling and tripling software sales annually, and maintaining cash flow positivity. The company has deployed 10 exabytes of data globally, with a 60% business focus on HPC and AI workloads.

Mao announces partnerships with cloud service providers specializing in AI workloads, such as Lambda, Core42, and Genesis Cloud, and mentions enterprise customers like Zoom and Pixar using VAST for AI/ML workloads. He shares the company’s origins, founded on the vision of creating a data center scale computer or “thinking machine,” and outlines the roadmap from storage systems to data management capabilities and transactional storage systems.

VAST Data’s architecture, the disaggregated shared everything model, separates logic from storage, allowing for scalability, reliability, and economic efficiency. This architecture underpins the company’s new capabilities, including a SQL-compliant, transactional, and scalable database called VAST Database, and the VAST Data Engine for event-driven processing. Mao discusses the integration of Apache Spark and Kafka into their platform, enabling containerized compute engines alongside storage. This approach allows for complex data workflows, such as triggering functions upon data ingestion for processing and metadata generation, aiming to provide a comprehensive data platform that extends beyond traditional storage solutions.