Microsoft & VAST Data partner to bring AI OS to Azure cloud
Microsoft and VAST Data have entered into a collaboration to make VAST Data's AI operating system available within the Azure cloud environment. The move will enable enterprises, particularly those in the Asia-Pacific region, to deploy high-performance, scalable infrastructure for complex artificial intelligence workloads directly on Azure.
AI OS on Azure
The integration will allow organisations to access VAST's full suite of data services natively in the Azure cloud. These include unified storage, data cataloguing, and database capabilities. The solution is designed to support the demands of agentic AI workflows, where autonomous agents interact with real-time data across different computing environments.
Through this collaboration, customers will be able to manage data across on-premises, hybrid, and multi-cloud environments without the need for extensive reconfiguration. VAST's platform will also run on Azure infrastructure using established governance, security, and billing systems familiar to Azure users.
Enterprise data services
The VAST AI operating system brings several features to Azure customers, including VAST InsightEngine and AgentEngine. InsightEngine is intended for high-performance compute and database tasks such as vector search, retrieval-augmented generation (RAG) pipelines, and data preparation. AgentEngine focuses on orchestrating autonomous agents to carry out continuous reasoning operations on real-time data streams, spread across hybrid and multi-cloud settings.
The DataSpace functionality enables exabyte-scale data management with a global namespace, aiming to eliminate silos. Customers can scale workloads from on-premises systems into Azure's GPU-accelerated infrastructure instantly, targeting AI model training and inference operations that require consistent, high-throughput performance at all stages.
Unified architecture
VAST's DataStore provides support for multiple data protocols, including file (NFS, SMB), object (S3), and block storage. This design is intended to let diverse workloads operate on a single platform. The VAST DataBase combines transactional processing with analytics and data lake economics, allowing applications to access and process large data sets without moving between multiple systems.
The platform uses a Disaggregated, Shared-Everything (DASE) architecture, separating compute and storage resources for independent scaling. Built-in Similarity Reduction is included to optimise the storage footprint, which is expected to reduce the costs for large-scale AI workloads.
Performance and scalability
The integration is optimised for the latest Azure Infrastructure, including the Laos VM Series and Azure Boost Accelerated Networking. This combination is intended to keep GPU and CPU resources highly utilised with efficient data delivery.
"Many AI model builders in the world leverage VAST for its scalability, breakthrough performance, and AI-native capabilities. This collaboration can help our mutual customers streamline operations, reduce costs, and accelerate time-to-insight for AI workloads of every size," said Aung Oo, Vice President, Azure Storage, Microsoft.
Strategic alignment
As Microsoft expands its ambitions in AI and custom silicon, VAST Data will work closely with Azure engineers to ensure alignment with emerging infrastructure requirements. The aim is to position VAST as a key platform to support the growing complexity and scale of AI systems in the cloud.
"This collaboration with Microsoft reflects our shared vision for the future of AI infrastructure, where performance, scale, and simplicity converge to enable enterprises to transform their business with agentic AI," said Jeff Denworth, Co-founder, VAST Data.
