For Cloud-Native Infrastructure Management

Go Cloud Native
In The Cloud Or
On-Prem

Leverage the Rafay Platform to standardize and centralize landing zones and
Kubernetes environments, deliver self-service workflows, and keep infrastructure costs low.

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Cloud-native application delivery is simpler and more secure.

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.

Standardize and centralize Kubernetes management

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.

Put EKS/AKS/GKE lifecycle management on autopilot

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.

More on public cloud infrastructure management

Operate an EKS-like service in your data center

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.

More on private cloud infrastructure management

Rightsize Kubernetes clusters and apps proactively

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.

Focus on innovation, not on Kubernetes automation

The Rafay Platform stack helps platform teams manage Kubernetes and cloud environments, across
all private and public clouds–helping companies realize the following benefits:

Unified Infrastructure Control

Manage Kubernetes environments consistently across cloud (EKS, GKE, AKS) and on-prem environments.

Data Sovereignty & Isolation

Enforce strict data residency, security, and workload isolation—ideal for regulated industries.

Air-Gapped Operability

Enable disconnected environments with complete lifecycle management, even without internet access.

Tooling Compatibility

Seamlessly integrate with enterprise CI/CD pipelines, observability tools, networking frameworks, and security stacks.

Developer Empowerment

Provide GPU-powered, secure, and compliant workspaces for AI/ML development with streamlined, self-service access.

Higher GPU Utilization, Lower Costs

Drive up GPU usage and eliminate idle capacity with automated provisioning and shared resource pools—reducing operational overhead and TCO.

Download the White Paper

Scale AI/ML Adoption

Delve into best practices for successfully leveraging Kubernetes and cloud operations to accelerate AI/ML projects.

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The pressure to deliver on the promise of AI has never been greater. Enterprises must find ways to make effective use of their GPU infrastructure to meet the demands of AI/ML workloads and accelerate time-to-market. Yet, despite making… Read More

GPU PaaS™ (Platform-as-a-Service) for AI Inference at the Edge: Revolutionizing Multi-Cluster Environments

April 19, 2025 / by Mohan Atreya

Enterprises are turning to AI/ML to solve new problems and simplify their operations, but running AI in the datacenter often compromises performance. Edge inference moves workloads closer to users, enabling low-latency experiences with fewer overheads, but it’s traditionally… Read More

Democratizing GPU Access: How PaaS Self-Service Workflows Transform AI Development

April 11, 2025 / by Gautam Chintapenta

A surprising pattern is emerging in enterprises today: End-users building AI applications have to wait months before they are granted access to multi-million dollar GPU infrastructure. The problem is not a new one. IT processes in… Read More