VAST AI Operating System

What it does

VAST AI Operating System ingests structured and unstructured data, including video, documents, audio, telemetry, and streaming events, from block, file, and object sources. It stores that data in a shared storage layer, generates vector embeddings and indexed database tables as data arrives, and runs event-triggered pipelines and AI agent workflows across GPUs.

Teams use it as a single platform in place of separate storage tiers, vector databases, streaming systems, ETL tooling, and agent deployment systems. Its distinguishing claim is that compute and data span edge and cloud through one global namespace, so GPUs can reach data wherever it sits. It is built on an architecture called DASE that separates compute from capacity.

It suits AI infrastructure builders, cloud providers, enterprise leadership, and technical implementers planning large-scale AI deployments. It can be deployed on-premises, in public clouds (AWS, Azure, GCP, Oracle), at the edge, or in hybrid setups, with hardware partners including HPE, Cisco, Dell, and Lenovo. Pricing is not published, so buyers should confirm deployment and consumption costs with VAST.

What it can do

• Unified storage for block, file, and object data (DataStore)
• Embedding generation and transactional/analytical database with vector search (DataBase)
• Event-triggered pipelines with a built-in Kafka-compatible message bus (DataEngine)
• Federated global namespace spanning clusters, data centers, and edge/cloud (DataSpace)
• Managed container runtime for scaling AI agent workflows across GPUs
• Support for NFS, SMB, S3, NVMe over TCP, SQL, and Kafka-compatible streaming

Works with

• Kubernetes clusters
• Model Context Protocol (MCP)
• Kafka-compatible streaming
• AWS
• Azure
• GCP
• Oracle Cloud

Before you choose

• The page presents performance and cost claims (e.g., 60% lower TCO, 20x faster analytics vs. Iceberg stacks) as vendor-reported figures, not independently verified results.
• Pricing is not published; costs depend on deployment and consumption options that require a sales conversation.
• Integration with existing infrastructure is described as possible without rip-and-replace, but the extent of migration effort for a given environment is not specified on the page.

Listed under

• Coding
• MCP and agent tools
• AI agents
• Data storage
• Enterprise AI infrastructure
• GPU compute
• Sovereign AI
• Streaming

Get started

The page directs visitors to a conversation with a solutions engineer; no self-serve sign-up is shown.

1. Assess your current stack, including storage tiers, vector databases, streaming platforms, and ETL tools that the AI OS is designed to consolidate.
2. Choose a deployment target among on-premises data centers, public clouds, edge locations, or a hybrid setup.
3. Contact VAST through the page's "Talk to an Expert" option so a solutions engineer can walk through your workload and deployment model.

Costs depend on deployment and consumption options; contact VAST to confirm pricing for your environment.

We couldn’t find published prices for VAST AI Operating System.

Sources for this overview

Product pages, documentation, and pricing checked 29 Oct 2026. Each source supports part of the overview; availability and prices can change.

• The AI Operating System (AI OS) - VAST Data ↗www.vastdata.com

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Last checked 29 Oct 2026. Every field keeps its source and date; unknown means we couldn’t confirm it.