Accelerate AI/ML Adoption

Accelerate AI Adoption with a
GPU PaaS and MLOps Tooling

Easily manage the underlying AI infrastructure and AI/ML
tooling your data scientists need to innovate faster, with guardrails included

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Is your adoption of AI fast enough?

Rafay empowers companies to accelerate AI adoption by providing a robust platform for seamless deployment, management, and scaling of AI/ML workloads across public cloud and on-premise environments. By making GPU-based infrastructure cheaper and easier to consume, along with providing AI/ML tooling for data scientists, Rafay enables organizations to speed up their AI adoption, driving innovation and a clear competitive edge.

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 development, not on infrastructure

Rafay helps platform teams build an enterprise-class AI practice
for their organizations while realizing 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 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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Most Recent Blogs

Image for Rafay and Netris: Partnering to speed up consumption and monetization for GPU Clouds

Rafay and Netris: Partnering to speed up consumption and monetization for GPU Clouds

March 12, 2025 / by Haseeb Budhani

Rafay, a pioneer in delivering platform-as-a-service (PaaS) capabilities for self-service compute consumption, and Netris, a leader in networking Automation, Abstraction, and Multi-tenancy for AI & Cloud operators , are collaborating to help GPU Cloud Providers speed up consumption… Read More

Image for Is Fine-Tuning or Prompt Engineering the Right Approach for AI?

Is Fine-Tuning or Prompt Engineering the Right Approach for AI?

March 6, 2025 / by Rajat Tiwari

While prompt engineering is a quick and cost-effective solution for general tasks, fine-tuning enables superior AI performance on proprietary data. We previously discussed how building a RAG-based chatbot for enterprise data paved the way for creating a… Read More

Image for GPU PaaS Unleashed: Empowering Platform Teams to Drive Innovation

GPU PaaS Unleashed: Empowering Platform Teams to Drive Innovation

December 18, 2024 / by Mohan Atreya

GPUs underpin cutting-edge AI, machine learning, and big data workloads. They also provide critical acceleration for simulation, video rendering, and streaming tasks. With modern enterprises likely to be investing in some or all of these fields, easy access… Read More

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