VAST Data look at future innovations for AI and the AI OS with Solidigm

An update from Vast Data on plans for 2026, efforts to help customers evolve their existing systems, and an early overview of the Vast AI OS platform. This session covers existing, soon-to-be-released, and future topics that will leave listeners wanting more, with more to come at an upcoming Vast event. Vast Data, founded in 2016, launched its initial storage product in 2019, based on a new shared-everything architecture designed to address the scale and efficiency challenges of migrating legacy systems into the AI era. Since then, the company has expanded to include a database product for structured and unstructured data, and is now integrating compute capabilities to enable customers to execute models and build agents. A significant focus is on addressing the pervasive “shared nothing” architecture, which, beyond storage, creates substantial problems in eventing infrastructure, leading to scaling difficulties, high write amplification from replication, and weak analytics capabilities, often causing significant delays in gaining real-time insights.

Vast Data’s shared-everything architecture aims to address these issues by providing a parallel compute layer that is ACID-compliant, ensuring event order across partitions. By treating eventing topics as tables in the Vast database, with each event as a row, they leverage storage-class memory for rapid data capture in row format, then migrate it to QLC in columnar format for robust analytics. This approach dramatically simplifies eventing infrastructure, boosts scalability, and delivers superior performance, achieving 1.5 million transactions per server and significantly reducing server count compared to legacy systems. The same “shared nothing” paradigm also plagues vector databases, leading to memory-bound systems that require extensive sharding, suffer from slow inserts and updates, and struggle to scale for rich media such as video, where vector counts can reach trillions.

Vast Data’s vector database, built on its unified architecture, addresses these challenges by supporting trillions of vectors within a single, consolidated database, eliminating the need for complex sharding. This enables seamless scalability for vector search and rapid inserts, a critical capability for real-time applications such as analyzing live video feeds, where traditional in-memory vector databases often fail. Furthermore, a key innovation is the unified security model, which applies a consistent permission structure from the original data (documents, images, videos) to their derived vectors. This ensures that large language models only access information authorized for the user, preventing unintended data exposure and maintaining robust data governance. The platform also supports data-driven workflows, automatically triggering processes such as video embedding and vector storage when new data arrives.