GPU/Neocloud Billing using Rafay’s Usage Metering APIs
Cloud providers offering GPU or Neo Cloud services need accurate and automated mechanisms to track resource consumption.
use case - FOR AI INFRASTRUCTURE MANAGEMENT
Accelerated Computing Infrastructure Management at Your Fingertips
Delivering AI use cases to market faster is a constant request for enterprises and cloud service providers who are either looking to accelerate application delivery internally or do so for their customers to have a competitive advantage.
The Rafay Platform's vast library of Generative AI, compute consumption, and infrastructure management built in offers customers "as a Service" experiences at every layer of the stack, including ready-made templates for GenAI use cases to speed up their enterprise AI journey.
Learn more about AI infrastructure management
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features
Transform Your AI Infrastructure Management Today
Launch GPU-as-a-Service, Serverless Inferencing, and AI Marketplaces in days—not months with the Rafay Platform. Deliver self-service environments (EaaS) for developers, ML teams, and platform users while supporting AI/ML training, model deployment, and GenAI inference across multiple environments.
Self-Service Experience
Developers and data scientists can deploy, view, and manage their GenAI applications and infrastructure in isolation using self-service workflows.
Environment Templates for Any Cloud or On-Prem Infrastructure
Teams can create environment and Kubernetes blueprints that brings standardization and consistency across any EKS, AKS, GKE or private data center or edge location.
Multi-tenancy for AI/ML Apps
It is incredibly common for enterprises to have different teams share clusters – perhaps with specific LLM resources – in an effort to save costs. The Rafay Platform's multi-modal, multi-tenancy capabilities can easily support many AI/ML teams on the same Kubernetes cluster.
Benefits
Leverage the Power of GenAI
Experience unparalleled efficiency and cost savings with AI infrastructure management features that simplify operations while enhancing performance across all environments.
- Faster development and time-to-market for all AI/ML applications
- Realize the business benefits of GenAI sooner
- Democratization of data and AI skills
Trusted by leading enterprises, neoclouds and service providers
Blog
AI Infrastructure Management - Latest Insights and Trends
Explore the latest trends in AI infrastructure management.
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Questions and answers about AI infrastructure management
Find answers to your most pressing questions about self-service compute consumption.
What is self-service compute?
AI Infrastructure refers to the underlying systems and technologies that support AI applications. This includes hardware, software, and network resources that enable data processing and machine learning. A robust AI Infrastructure is essential for efficient and scalable AI deployments.
How does it work?
The benefits of a well-structured AI Infrastructure include improved efficiency, scalability, and cost-effectiveness. It enables organizations to leverage AI capabilities without significant upfront investments. Additionally, it supports faster decision-making and innovation.
Is it scalable?
Yes, AI Infrastructure is designed to be scalable. Organizations can easily expand their resources to accommodate growing data and processing needs. This flexibility ensures that businesses can adapt to changing demands without disruption.
How to get started?
To get started with AI Infrastructure, assess your organization's needs and goals. Next, choose the right tools and technologies that align with your objectives. Finally, implement a strategy that includes training and support for your team.
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