Enterprises and cloud providers use the Rafay Platform to support the most critical cloud-native and AI use cases. The Rafay Platform provides self-service consumption of compute and application workflows, along with complete lifecycle management of Kubernetes clusters, GPUs, and bare metal servers.
With cost optimization and multi-tenancy capabilities built in, Rafay customers can deliver CSP-grade environments and experiences for self-service consumption by developers and data scientists in weeks, not years. Customers report 4x faster deployments, 63% lower cloud costs, and 85% less time spent on upgrades.
Combat infrastructure lifecycle management complexity with the Rafay Platform. Customers can transform static compute into enterprise-grade, centrally governed, self-service environments.
Developers gain on-demand access to Kubernetes clusters, namespaces (with optional virtual cluster support), full-featured test beds, and a variety of services that drive developer productivity to new heights. Data scientists instantly access notebooks, inference endpoints with their choice of models, and GPU slices to develop new capabilities, all with compliance and cost controls built in.
The result: Costly hardware is turned into a means for generating business value and higher revenues, and infrastructure becomes a launchpad for cloud-native and AI innovation.
Centrally enforce enterprise-grade policies and CCOE-approved configuration and software manifests, cost controls, etc. the latest addons, policies and cost controls across all environments, clusters and landing zones.
Automate the lifecycle management of managed Kubernetes services available in public clouds (e.g. EKS, AKS, & GKE) with enterprise-grade controls, highly customizable fleet management capabilities, and more.
Leverage Rafay’s turnkey, fully-managed Kubernetes distribution that scales to meet the needs of the largest enterprises and the most complex service provider environments.
Centralize visibility and attribution of costs to downstream customers and users, along with the application policies to keep overall costs in check.
Provide a turnkey MLOps platform to internal and external users with built-in AI workbenches that data scientists and researchers can leverage on demand across GPUs and CPUs.
Deliver self-service experience across public clouds and data center environments. Developers get self-service access to compute and tooling needed to move fast and experiment, while platform teams maintain full control, governance, and cost-efficiency.
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With a vast library of Generative AI, compute consumption and infrastructure management built in, enterprises and service providers can deliver “as a Service” experiences at each layer of the stack without investing in massive teams and multiple quarters.
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Cloud providers, neoclouds and Sovereign AI clouds who have partnered with Rafay are leading the charge to deliver CSP-grade use cases to their user communities. From agentic applications, ML workbenches, models as a service, to highly tuned virtual machines, K8s clusters and baremetal servers, Rafay is the partner of choice for the most innovative GPU providers in the world.
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Learn why so many enterprises and service providers choose the Rafay Platform for their modern infrastructure needs With the Rafay Platform, customers:
Understand what it takes to deliver the right GPU infrastructure to your business.
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