VAST Data AI Operating System

Unify data across on-prem and cloud VAST AI OS connects on-prem and cloud environments into a single Global Namespace for file and object access. Instead of creating separate copies on-prem and in cloud, workloads can access the same dataset regardless of location, while keeping consistent paths, metadata, and data services.

Simplify data operations for AI across hybrid cloud VAST turns enterprise data into continuously usable AI context across on-prem and cloud by maintaining current metadata, embeddings, and indexes alongside the data itself. Teams can ingest and enrich data once, keep derived representations refreshed as source data changes in either location, and reuse that context across projects without rebuilding pipelines when workloads shift between on-prem and cloud. This keeps retrieval grounded in what is current and reduces the operational friction of hybrid AI.

Build cloud-ready AI data services with a consistent platform layer VAST provides the AI data services production systems rely on, delivered consistently across clouds and integrated with hybrid deployments. Vector-aware search and retrieval can be operated as a shared service, with real-time and batch processing in the same environment, so teams avoid assembling separate indexing, caching, and database stacks for each cloud region or account. This makes it easier to standardize how applications produce and consume embeddings, manage derived context, and promote high-value outputs into reusable enterprise knowledge.

Operate with cloud-scale governance and operational visibility VAST enforces access controls and policy consistently across hybrid deployments, reducing the complexity of governing data as it spans on-prem and cloud environments. It provides centralized auditing and provenance, so teams can verify what data was accessed and trace outputs back to source material when needed for compliance, investigation, or quality review. For operational oversight, VAST exposes the signals teams rely on to monitor data and pipeline health across environments, helping operators detect drift, troubleshoot performance issues, and maintain predictable behavior as models, prompts, and workflows change.