网页
综合
更新时间 2026-11-13 21:42:26
Why Rafay – Infrastructure Orchestration & Automation | Rafay
Rafay's focus on infrastructure orchestration and workflow automation simplify the operations and management of complex infrastructure consisting of both GPUs and CPUs. Prioritizing self-service consumption of cloud-native and AI use cases, developers and data scientists gain immediate access to critical accelerated computing resources.
Rafay envisions a future where infrastructure is instantly consumable across GPUs, CPUs, and any compute environment. Self-service replaces ticket queues and manual provisioning, while enterprise-grade governance is intrinsic. This ensures soaring utilization of scarce resources, enabling enterprises to maximize investments. At the same time, cloud service providers achieve higher margins and utilization by launching GPU Clouds across the globe.
Is it difficult to dynamically allocate GPUs for data scientists and enable expeditious AI application delivery?
Are there infrastructure constraints preventing a team's abilities to efficiently manage cluster lifecycles for EKS, AKS, or GKE?
Are teams buried in manual tasks and updates for managing the Kubernetes lifecycle in a private data center?
Is it difficult to centrally define and enforce consistent configs and workflows across your entire infrastructure?
Is it difficult to dynamically allocate GPUs for data scientists and enable expeditious AI application delivery?
The flow chart diagram below is a typical journey Rafay supports for enterprises and cloud providers. First, customers standardize environments across all public and private clouds, centrally manage the lifecycle of all resources, and continuously optimize cloud costs. Next, customers enable the same for AI operations and provide self-service capabilities to developers and data scientists.
The Rafay Platform simplifies Kubernetes and AI infrastructure lifecycle operations across public clouds, private data centers, sovereign deployments, and the edge. Users can securely pool GPUs and CPUs into multi-tenant resources accessible across teams, while organizations can deliver custom compute and AI service SKUs on demand. By powering developer-ready experiences through a composable marketplace, enterprises not only operationalize infrastructure but also monetize it.
Rafay reduces that complexity with an infrastructure orchestration and workflow automation platform–improving developer productivity, optimizing cluster and pipeline management processes, and launching multi-tenant capable GPU clouds in weeks, not years.
Unlike hyperscaler-native tools (e.g., AWS SageMaker, GCP Vertex AI, Azure ML) that are cloud-specific, Rafay’s unified PaaS stack supports Kubernetes, CPU, and GPU infrastructure across on-prem, public clouds, and air-gapped environments—delivering true hybrid and multi-cloud flexibility.
DIY Kubernetes setups or general-purpose platforms like Rancher, OpenShift, or Kubeflow often require complex configuration and maintenance. Rafay offers policy-driven automation, self-service access, and integrated developer tooling—removing operational bottlenecks and accelerating AI/ML delivery.
While traditional platforms were designed for generic workloads, Rafay is purpose-built for GPU orchestration, distributed training, and GenAI inferencing—providing intelligent scheduling, cost attribution, and multi-tenant controls not available out-of-the-box with open-source stacks.
With built-in cost optimization, metering, and resource right-sizing, Rafay enables enterprises and GPU cloud providers to monetize idle GPU infrastructure quickly—turning CapEx into revenue-generating services far faster than internal builds or hyperscaler-native deployments.
When blogs and community resources aren’t enough, partner with Rafay’s deep bench of certified cloud automation experts to jump-start & customize your application modernization journey.
across mission critical workloads for oncology R&D and analytics
from being spent on excess cloud costs and operational inefficiency
which empowers developers to iterate faster and accelerate their innovation
Understand what it takes to deliver the right GPU infrastructure to your business.
Rafay envisions a future where infrastructure is instantly consumable across GPUs, CPUs, and any compute environment. Self-service replaces ticket queues and manual provisioning, while enterprise-grade governance is intrinsic. This ensures soaring utilization of scarce resources, enabling enterprises to maximize investments. At the same time, cloud service providers achieve higher margins and utilization by launching GPU Clouds across the globe.
Is it difficult to dynamically allocate GPUs for data scientists and enable expeditious AI application delivery?
Are there infrastructure constraints preventing a team's abilities to efficiently manage cluster lifecycles for EKS, AKS, or GKE?
Are teams buried in manual tasks and updates for managing the Kubernetes lifecycle in a private data center?
Is it difficult to centrally define and enforce consistent configs and workflows across your entire infrastructure?
Is it difficult to dynamically allocate GPUs for data scientists and enable expeditious AI application delivery?
The flow chart diagram below is a typical journey Rafay supports for enterprises and cloud providers. First, customers standardize environments across all public and private clouds, centrally manage the lifecycle of all resources, and continuously optimize cloud costs. Next, customers enable the same for AI operations and provide self-service capabilities to developers and data scientists.
The Rafay Platform simplifies Kubernetes and AI infrastructure lifecycle operations across public clouds, private data centers, sovereign deployments, and the edge. Users can securely pool GPUs and CPUs into multi-tenant resources accessible across teams, while organizations can deliver custom compute and AI service SKUs on demand. By powering developer-ready experiences through a composable marketplace, enterprises not only operationalize infrastructure but also monetize it.
Rafay reduces that complexity with an infrastructure orchestration and workflow automation platform–improving developer productivity, optimizing cluster and pipeline management processes, and launching multi-tenant capable GPU clouds in weeks, not years.
Unlike hyperscaler-native tools (e.g., AWS SageMaker, GCP Vertex AI, Azure ML) that are cloud-specific, Rafay’s unified PaaS stack supports Kubernetes, CPU, and GPU infrastructure across on-prem, public clouds, and air-gapped environments—delivering true hybrid and multi-cloud flexibility.
DIY Kubernetes setups or general-purpose platforms like Rancher, OpenShift, or Kubeflow often require complex configuration and maintenance. Rafay offers policy-driven automation, self-service access, and integrated developer tooling—removing operational bottlenecks and accelerating AI/ML delivery.
While traditional platforms were designed for generic workloads, Rafay is purpose-built for GPU orchestration, distributed training, and GenAI inferencing—providing intelligent scheduling, cost attribution, and multi-tenant controls not available out-of-the-box with open-source stacks.
With built-in cost optimization, metering, and resource right-sizing, Rafay enables enterprises and GPU cloud providers to monetize idle GPU infrastructure quickly—turning CapEx into revenue-generating services far faster than internal builds or hyperscaler-native deployments.
When blogs and community resources aren’t enough, partner with Rafay’s deep bench of certified cloud automation experts to jump-start & customize your application modernization journey.
across mission critical workloads for oncology R&D and analytics
from being spent on excess cloud costs and operational inefficiency
which empowers developers to iterate faster and accelerate their innovation
Understand what it takes to deliver the right GPU infrastructure to your business.
本页为82搜索生成的网页快照，内容来自站内索引；演示数据的原文链接仅作示例。