The AI & Cloud-Native Infrastructure Blog

Simplifying AI Workload Delivery for Platform Teams in 2025

AI workloads are growing more complex by the day, and platform teams are under immense pressure to deliver them at scale—securely, efficiently, and with speed.

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Product

Get Started with BioContainers using Rafay

In this step-by-step guide, the Bioinformatics data scientist will use Rafay’s end user portal to launch a well resourced remote VM and run a series of BioContainers with Docker.

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BioContainers: Streamlining Bioinformatics with the Power of Portability

In today’s fast-paced world of bioinformatics, the constant evolution of tools, dependencies, and operating system environments presents a significant challenge.

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Why GPUs Are Essential for AI Workloads

As artificial intelligence and machine learning continue to evolve, one thing has become clear: not all infrastructure is created equal.

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IaaS vs PaaS vs SaaS: The Cloud Computing Stack Demystified

In today’s cloud-first world, understanding the differences between Infrastructure as a Service (IaaS), Platform as a Service (PaaS), and Software as a Service (SaaS) is essential for IT decision-makers. These three core cloud models form the backbone of digital transformation, each providing unique benefits and levels of abstraction.

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What Is Platform as a Service (PaaS)?

What Is Platform as a Service (PaaS)? Platform as a Service (PaaS) is a cloud computing model, often referred to as the PaaS model, that provides a robust framework for developers to build, test, deploy, and manage applications efficiently.

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Product

What Is GPU PaaS?

GPU Platform as a Service (GPU PaaS) is a cloud-native model that gives developers and data scientists secure, on-demand access to GPU resources for running AI, GenAI, and ML workloads. Rafay’s GPU PaaS™ stack simplifies GPU delivery across any environment—enabling faster time-to-market and maximum return on GPU investments, allowing you to immediately monetize your GPU (which historically are quite expensive and difficult to access).

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Introducing Serverless Inference: Team Rafay’s Latest Innovation

The GenAI revolution is in full swing, and for NVIDIA Cloud Partners (NCPs), GPU Cloud Providers (aka GPU Clouds), and Sovereign Cloud operators, it presents a significant opportunity. To keep up with market demands, NCPs and GPU Clouds are looking to go beyond simply selling GPUs—they are looking for ways to deliver managed GenAI services to help customers speed up AI adoption, while boosting their operating margins.

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

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.

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

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 cumbersome to manage GPUs (Graphics Processing Units) in distributed infrastructure.

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

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.

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

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 and monetization of GPU-based infrastructure by offering self-service workflows for model training, finetuning and inferencing use cases. By partnering with Rafay and Netris, GPU Cloud Providers can go from GPUs to an enterprise-grade GPU Cloud in weeks.

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

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 comprehensive GenAI platform.

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GPU PaaS™ Unleashed: Empowering Platform Teams to Drive Innovation

GPUs underpin cutting-edge AI, machine learning, and big data workloads. They also provide critical acceleration for simulation, video rendering, and streaming tasks.

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Optimizing AI Workloads for Multi-Cloud Environments with Rafay and GPU PaaS

Rafay’s platform enables you build a GPU PaaS for AI workloads so you can confidently operate machine learning models, generative AI, and neural networks at scale. It orchestrates your hybrid and multi-cloud computing resources, improves operational flexibility, and includes precise governance controls to support continual compliance.

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Operationalizing AI: Solutions to Machine Learning Workflow Automation Challenges

Machine learning (ML) has emerged as a transformative force, enabling organizations to derive critical insights, enhance customer experiences, and make data-driven predictions. However, operationalizing machine learning workflows presents significant challenges, especially for enterprises with complex, cloud-based infrastructures.

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Achieving Optimal AI Performance with Tuning-as-a-Service

Tuning-as-a-Service (another TaaS but not to be confused with Training-as-a-service) is a cloud-based solution that optimizes AI models by automating the adjustment of hyperparameters to enhance model accuracy, efficiency, and overall performance. By leveraging advanced algorithms and scalable cloud resources, TaaS platforms streamline the often-complex and time-consuming process of model tuning , allowing organizations to achieve optimal AI performance without dedicating extensive manual effort.

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Optimizing AI Deployments with Training-as-a-Service Platforms

As artificial intelligence continues to reshape industries, the demand for efficient, scalable training solutions has surged. Training-as-a-Service (TaaS) platforms are emerging as essential tools for developers, data architects, and platform engineering teams working in AI model development.

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Unlocking the Potential of MLOps as a Service: Streamlining AI and ML Pipelines

A New Era in AI and ML Operations Managing ML models effectively is more crucial than ever in the rapidly evolving landscape of artificial intelligence (AI) and machine learning (ML). From healthcare to finance and retail, industries are leveraging machine learning models to make data-driven decisions, automate complex tasks, and provide personalized experiences.

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Training as a Service: Empowering AI Teams with Managed Model Training Solutions

Artificial intelligence is rapidly evolving, and the ability to efficiently train AI models is essential for competitive advantage. As applications scale, organizations face growing complexities around model training— from managing extensive datasets to securing infrastructure that supports continuous, high-performance training cycles.

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Building the Right Foundation: Key Infrastructure for MLOps Platforms

In today’s data-driven landscape, MLOps platforms have become essential for developers, data scientists, and engineering teams seeking to streamline machine learning (ML) workflows and drive impactful, scalable outcomes. These platforms bridge the gap between model development and deployment, enabling teams to manage the entire ML lifecycle with greater consistency, speed, and accuracy.

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Fine-Tuning AI Models with Tuning-as-a-Service Platforms

The adoption of AI models across enterprises has accelerated in recent years, with businesses leveraging artificial intelligence to streamline operations, improve customer interactions, and gain actionable insights. However, out-of-the-box AI solutions often lack the specificity and precision required for specialized tasks, which is where fine-tuning becomes essential.

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Unlocking the Potential of Inference as a Service for Scalable AI Operations

As artificial intelligence (AI) becomes more integral to business operations, organizations face mounting challenges in deploying models efficiently while keeping up with real-time performance demands. Traditional AI model deployment methods involve complex infrastructure management, requiring IT operations to handle everything from GPU provisioning to monitoring performance.

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Unlocking GPU Infrastructure Orchestration with Rafay

Platform teams today face mounting pressure to deploy, scale, and optimize GPU resources for complex AI workloads across hybrid and multi-cloud environments. Thankfully, Rafay enables customers to deploy a GPU PaaS that offers a streamlined solution, equipping enterprises with the tools to orchestrate GPU infrastructure seamlessly and efficiently.

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Explore the Latest Insights

Featured Blog

Powering GPU Cloud Billing: Rafay + Monetize360 Integration

Simplifying AI Workload Delivery for Platform Teams in 2025

Get Started with BioContainers using Rafay

BioContainers: Streamlining Bioinformatics with the Power of Portability

Why GPUs Are Essential for AI Workloads

IaaS vs PaaS vs SaaS: The Cloud Computing Stack Demystified

What Is Platform as a Service (PaaS)?

What Is GPU PaaS?

Introducing Serverless Inference: Team Rafay’s Latest Innovation

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

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

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

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

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

GPU PaaS™ Unleashed: Empowering Platform Teams to Drive Innovation

Optimizing AI Workloads for Multi-Cloud Environments with Rafay and GPU PaaS

Operationalizing AI: Solutions to Machine Learning Workflow Automation Challenges

Achieving Optimal AI Performance with Tuning-as-a-Service

Optimizing AI Deployments with Training-as-a-Service Platforms

Unlocking the Potential of MLOps as a Service: Streamlining AI and ML Pipelines

Training as a Service: Empowering AI Teams with Managed Model Training Solutions

Building the Right Foundation: Key Infrastructure for MLOps Platforms

Fine-Tuning AI Models with Tuning-as-a-Service Platforms

Unlocking the Potential of Inference as a Service for Scalable AI Operations

Unlocking GPU Infrastructure Orchestration with Rafay

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