Leverage the Rafay Platform to standardize and centralize landing zones and Kubernetes environments, deliver self-service workflows, and keep infrastructure costs low.
Enterprises running AI/ML workloads in private cloud environments face a constant trade-off: deliver fast, flexible infrastructure or maintain strict security, compliance, and operational control. Speed often comes at the cost of governance. Rafay eliminates that trade-off. With a powerful automation and orchestration layer, Rafay enables enterprise teams to run secure, compliant AI and GenAI workloads in private cloud environments—without compromising agility or slowing down innovation.
Rafay enables platform teams to standardize every aspect of a cloud environment’s configuration – cluster & environment configs, resource allocation, approved addons, access controls, security policies, and much more – in one place.
Rafay allows a small, central team to easily manage the lifecycle of all Kubernetes clusters and cloud resources being used by multiple BUs and hundreds of app teams, hosted across public clouds (AWS, Microsoft Azure, Google Cloud, Oracle Cloud and more). This enables end-to-end lifecycle management automations that eliminate the need for specialized teams.
Why not have the same, great, public cloud-like managed Kubernetes experience but in your own data center?
Rafay provides turnkey management of Kubernetes resources hosted in private, remote, or edge clouds (including bare metal, VMware vSphere, local VMs, and support for their networking and storage needs). Now, your team can focus on higher-value work rather than the menial tasks of keeping Kubernetes operational.
Optimize cloud cost by detecting and fixing resource allocation issues through intelligent policy-driven controls for workload management. This ensures Kubernetes and cloud resources are rightsized for better application utilization, performance and cost efficiency.
The Rafay Platform stack helps platform teams manage Kubernetes and cloud environments, acrossall private and public clouds–helping companies realize the following benefits:
Manage Kubernetes environments consistently across cloud (EKS, GKE, AKS) and on-prem environments.
Enforce strict data residency, security, and workload isolation—ideal for regulated industries.
Enable disconnected environments with complete lifecycle management, even without internet access.
Seamlessly integrate with enterprise CI/CD pipelines, observability tools, networking frameworks, and security stacks.
Provide GPU-powered, secure, and compliant workspaces for AI/ML development with streamlined, self-service access.
Drive up GPU usage and eliminate idle capacity with automated provisioning and shared resource pools—reducing operational overhead and TCO.
In this step-by-step guide, the Bioinformatics data scientist will use Rafay’s end user portal to launch a well resourced remote VM and run a series of BioContainers with Docker.
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What Is Platform as a Service (PaaS)? Platform as a Service (PaaS) is a cloud computing model, often referred to as the PaaS model, that provides a robust framework for developers to build, test, deploy, and manage applications efficiently.
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GPU Platform as a Service (GPU PaaS) is a cloud-native model that gives developers and data scientists secure, on-demand access to GPU resources for running AI, GenAI, and ML workloads. Rafay’s GPU PaaS™ stack simplifies GPU delivery across any environment—enabling faster time-to-market and maximum return on GPU investments, allowing you to immediately monetize your GPU (which historically are quite expensive and difficult to access).
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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!
