The AI Operating System

Most AI teams manage separate systems for storage, data ingestion, analytics, vector search, streaming, and AI runtime. The VAST AI OS pulls all of it into a single unified infrastructure layer. One platform: Simple. Reliable. Secure.

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Trusted by the World’s Leading Data-Driven Organizations

CACEIS
Evodian
SK Telecom
Voltage Park
SHARCNET
SciNet
BUZZ HPC
Sharon AI
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Capabilities

What the AI OS Can Do

The VAST AI Operating System captures any data at any scale, enriches it into meaning the moment it lands, and activates it across a global grid of GPUs.

The VAST AI OS powers the performance, simplicity, and flexibility needed to support the next generation of sovereign AI workloads, and gives us the confidence to scale fast and securely. With VAST, we’re enabling a GPUaaS platform that meets the exacting needs of government, research, and enterprise AI customers in South Korea.

DK Lee
Vice President, and Head of AI DC Lab, SK Telecom
Performance

Powering AI’s Most Demanding Production Environments

aggregate throughput

>11 TB/s

aggregate throughput

uptime across 3M+ GPUs in production

>99.999%

uptime across 3M+ GPUs in production

faster analytics vs. Iceberg stacks

20×

faster analytics vs. Iceberg stacks

lower TCO

60%

lower TCO

Technology

How the AI OS Works

Every capability in the VAST AI OS runs on a single architecture: DASE (Disaggregated Shared-Everything). DASE separates compute from capacity so each can scale independently, and it gives every node access to the full global dataset. That's how the AI OS keeps adding capabilities — database, event streaming, vector search, agent orchestration — without compromise.

DataStore

Unified Storage

Every workflow starts here. Enterprise data, whether block, file, or object, is written to the DataStore, a single exabyte-scale foundation that consolidates all of it without the tiering and silos that slow AI.

Best data reduction. Get the most value out of every PB of capacity you deploy.

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DataStore
DataBase

Transactional & Analytical Database

Next, data is chunked, embedded, and stored as vectors in the DataBase, alongside structured tables and streaming data, so unstructured and structured data streams are instantly ready for query and similarity search.

20× faster analytical queries vs. Iceberg stacks.

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Transactional & Analytical Database
DataEngine

AI Pipeline Automation

The DataEngine then acts on it, leveraging python functions and a built-in Kafka-compatible message bus, triggering agents the moment data changes to take actions directly against it, turning a passive store into an active platform.

Lower TCO and faster TTV with integrated Kafka and pipeline orchestration tools.

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DataEngine
DataSpace

Global Namespace

This makes that data available to the GPUs that need it, wherever they sit, leveraging a federated global namespace that spans clusters, within or across data centers, on-prem or off, from edge to cloud. Real-time access to any data, anywhere.

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DataSpace

Integrations

Native Integrations Across the AI Ecosystem

VAST connects to the tools your teams already use through native APIs, open-source plugins, and validated integrations. No rip-and-replace. Deploy alongside your current infrastructure and expand at your own pace.

VAST Data image

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Quick Answers

Common Questions About the AI OS

The AI OS is one unified platform that replaces dozens of point solutions, dramatically reducing complexity and vendor management overhead. Think about simplifying:

  • Multiple storage tiers and vendors 

  • Separate vector databases 

  • External streaming platforms

  • Complex ETL pipelines and data preparation tools

  • Disparate MLOps and agent deployment systems

AI Builders & Cloud Providers: Organizations creating and deploying large-scale AI models and cloud platforms need extreme performance, scalability, and advanced tooling to manage their next-gen AI services and agentic systems.

Enterprise Leadership: C-suite executives leveraging AI for their competitive advantage and business transformations need strategic solutions that deliver measurable ROI, robust security, and simplified infrastructure management.

Technical Implementers: AI engineers and architects building and maintaining AI/ML infrastructure need scalable platforms with efficient MLOps tools, simplified integration, and support for advanced AI capabilities, like real-time processing and vector databases for RAG.

Learn more about our flexible deployment and consumption options.

Runs everywhere AI lives: on-premises data centers, public clouds (AWS, Azure, GCP, Oracle), edge locations, or hybrid environments. Software-first architecture adapts to your infrastructure, not the other way around.

Hardware Compatibility: Works efficiently on commodity hardware platforms with established partnerships including HPE, Cisco, Dell, Lenovo, and more to ensure smooth deployment across different environments.

Protocol Support: Natively supports all major enterprise protocols including NFS, SMB, S3, NVMe over TCP, SQL, and Kafka-compatible streaming, so your existing applications and workflows continue to work.

System Integration: Integrates with existing Kubernetes clusters and bridges legacy applications through real-time message brokers. Built on industry standards like Model Context Protocol (MCP) for maximum interoperability with current AI tools.

Security Continuity: Unified identity management and end-to-end encryption work with your existing security frameworks while extending governance across the entire AI environment.

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