Rafay-powered Virtual Machines as a Service (VMaaS) enables organizations to deliver GPU- or CPU-based virtual machines as secure, scalable, and consumption-based services.
Traditional VM offerings often lead to slow provisioning, poor utilization, and limited differentiation. Rafay transforms existing compute capacity into fully managed, tenant-ready VM SKUs, providing governance, visibility, and automated lifecycle control.
Service providers, enterprises, and sovereign cloud operators can offer high-performance VMs on demand, achieving faster service rollout, higher utilization, and greater operational efficiency.
Self-Service Provisioning: Tenants can configure and launch virtual machines instantly through an intuitive portal.
Elastic Scaling: Allocate between one and eight compute units per VM to meet AI, ML, or HPC workload needs.
Integrated Governance: Unified visibility, usage-based billing, and chargeback support for accurate accounting.
Rafay streamlines the complexity of provisioning and managing virtual machines across diverse environments.
Ready-to-use images with network and driver integrations for faster onboarding
Turnkey connections to Ceph, DDN, Weka, Vast, Dell PowerStore, and other platforms
Real-time tracking of performance and usage for precise reporting
VPC creation, public IP assignment, and firewall management for isolated deployments
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Aviz ONES, NVIDIA Spectrum-X Ethernet, and the Rafay Platform deliver a unified solution turning GPU fabrics into governed, cloud-native environments, instantly consumable.
Enterprises and Cloud Providers leverage the Rafay Platform to deliver self-service consumption of AI and cloud-native infrastructure to developers and data scientists.
Cisco provides AI-optimized infrastructure. The Rafay Platform makes it usable across teams, tenants, and use cases in days.
Learn how the Rafay Platform "allows CIOs to align their AI strategies with national regulatory frameworks while maintaining global scalability and agility" in this analyst report.
The latest Radar Report from GigaOm, the Rafay Platform's "Managed Kubernetes" capability is ranked as an “Outperformer."
CSPs can harness these strategic opportunities to capitalize on the AI-optimized IaaS market, projected at $80 billion in 2028.
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“Rafay’s central orchestration platform facilitates efficient, self-service infrastructure and AI application management” writes Accenture | NVIDIA in 2025 Building AI Value Within Borders paper.
Learn how to accelerate the ROI of your GPU infrastructure by quickly delivering a fully functional GPU Cloud with the self-service workflows and infrastructure orchestration of the Rafay Platform.
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This paper explores the key challenges that organizations experience supporting these initiatives, as well as best practices for successfully leveraging Kubernetes to accelerate AI/ML projects.
"We are able to deliver new, innovative products and services to the global market faster and manage them cost-effectively with Rafay"
"We are able to deliver new, innovative products and services to the global market faster and manage them cost-effectively with Rafay"
"We are able to deliver new, innovative products and services to the global market faster and manage them cost-effectively with Rafay"
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As a sponsor of Gartner IOCS 2025, Rafay highlights why modern I&O needs a platform operating model to keep pace with cloud-native and AI workloads.
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The Rafay Partner Elevate Program is designed to empower our global ecosystem of partners from resellers and system integrators to managed service providers, to deliver cutting-edge AI, cloud, and Kubernetes outcomes faster and more profitably.
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This blog details the specific features of the Rafay Platform Version 4.0 Which Further Simplifies Kubernetes Management and Accelerates Cloud-Native Operations for Enterprises and Cloud Providers
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See for yourself how to turn static compute into self-service engines. Deploy AI and cloud-native applications faster, reduce security & operational risk, and control the total cost of Kubernetes operations by trying the Rafay Platform!
