For AI Infrastructure Management

Accelerate AI Adoption with a
GPU PaaS™ and MLOps Tooling

The Rafay Platform stack helps instantly monetize GPU infrastructure and speed up AI application delivery–while unlocking new revenue streams, improving profitability, and keeping systems secure.

GPU PaaS Use Case Download the RA

AI application delivery has never been easier.

While many GPUs are underutilized, The Rafay Platform stack ensures AI application delivery is faster, more accurate, and more secure than ever–giving companies the competitive edge they need to take hold of evolving GenAI initiatives in the business. Whether a GPU cloud or sovereign cloud provider, The Rafay Platform supports national data sovereignty, residency, and compliance requirements so teams can worry less about infrastructure, and focus their energy on innovation.

Launch a customizable GPU PaaS in days

Accelerate your time-to-market with high-value NVIDIA hardware by rapidly launching a PaaS for GPU consumption, complete with a customizable storefront experience for your internal and external customers.

Deliver a SageMaker-like experience anywhere

Transform the way you build, deploy, and scale machine learning with Rafay’s comprehensive MLOps platform that runs in your data center and any public cloud.

Provide self-service AI Workbenches to data scientists

Data scientists can quickly access a fully functional data science environment without the need for local setup or maintenance. They can be more productive, sooner, by focusing on coding and analysis rather than managing AI infrastructure.

Consume a scalable, cost-effective GenAI playground to enable experimentation

Help developers experiment with GenAI by enabling them to rapidly train, tune, and test large models, along with approved tools such as vector databases, inference servers, etc.

Focus on AI innovation, not infrastructure

The Rafay Platform stack helps platform teams manage AI initiatives across
any environment–helping companies realize the following benefits:

Harness the Power of AI Faster

Complex processes and steep learning curves shouldn’t prevent developers and data scientists from building AI applications. A turnkey MLOps toolset with support for both traditional and GenAI (aka LLM-based) models allows them to be more productive without worrying about infrastructure details

Reduce the
Cost of AI

By utilizing GPU resources more efficiently with capabilities such as GPU matchmaking, virtualization and time-slicing, enterprises reduce the overall infrastructure cost of AI development, testing and serving in production.

Increase Productivity for Data Scientists

Provide data scientists and developers with a unified, consistent interface for all of the MLops and LLMOps work regardless of the underlying infrastructure, simplifying training, development, and operational processes.

Download the Reference Architecture
GPU PaaS Reference Architecture

AI application delivery has never been easier. Download the blueprint today.

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Most Recent Blogs

Image for Experience What Composable AI Infrastructure Actually Looks Like — In Just Two Hours

Experience What Composable AI Infrastructure Actually Looks Like — In Just Two Hours

April 24, 2025 / by

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

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

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

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

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

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