Top 50 Ai Gpu Cloud

Provides a fully managed AI cloud platform powered by NVIDIA® H100 and H200 Tensor Core GPUs, offering scalable GPU clusters with InfiniBand networking for high-speed data processing. Enables efficient model training, fine-tuning, and inference with tools like MLflow, PostgreSQL, and Apache Spark, reducing the complexity and cost of deploying AI applications at scale.

Nscale provides a GPU cloud platform optimized for AI workloads, featuring on-demand compute and inference services, dedicated training clusters, and scalable GPU nodes. The platform addresses the high costs and inefficiencies associated with AI model training and deployment by offering a fully integrated infrastructure powered by renewable energy in Europe.

Hyperbolic provides an open-access AI cloud that aggregates global GPU resources, enabling users to run AI inference and access compute power at significantly reduced costs. The platform addresses the high expenses associated with traditional cloud services by offering flexible, pay-as-you-go GPU access and opportunities for individuals and data centers to monetize idle machines.

Genesis Cloud provides a GPU cloud platform built on NVIDIA's reference architecture, delivering up to 35 times more performance for AI and machine learning workloads at 80% lower costs compared to traditional cloud providers. The platform ensures high security and compliance with EU regulations, enabling enterprises to efficiently manage and scale their AI applications.

GMI Cloud provides instant access to NVIDIA H100 GPUs for training and deploying generative AI applications, utilizing a Kubernetes-based cluster engine for efficient workload orchestration. This platform addresses the need for rapid GPU provisioning and management, enabling developers to focus on building AI models without the complexities of infrastructure setup.

Lepton AI Cloud provides a scalable platform for AI inference and training, utilizing high-performance GPU infrastructure and a fast LLM engine to achieve up to 600 tokens per second. The platform enables enterprises to efficiently deploy and manage AI models, processing over 20 billion tokens and generating 1 million images daily with 99.9% uptime.

Crusoe provides a managed AI cloud platform that delivers low‑latency, high‑throughput inference for large‑context models using NVIDIA and AMD GPUs with its MemoryAlloy engine. The service abstracts cluster provisioning via an API‑key workflow, auto‑scales on Kubernetes/Slurm, and includes a web console for one‑click model deployment, while its renewable‑powered data centers reduce compute costs by up to 80 %.

RunPod is a cloud platform that provides globally distributed GPU resources for deploying and scaling machine learning applications, enabling developers to run AI workloads without managing infrastructure. The platform reduces cold-start times to under 250 milliseconds and offers flexible pricing, allowing users to efficiently handle fluctuating demand while minimizing operational costs.

Together AI provides a cloud-based platform for training, fine-tuning, and deploying open-source generative AI models using NVIDIA's latest GPUs, enabling users to achieve high performance at a lower cost. The platform addresses the need for scalable AI infrastructure by offering customizable solutions that support the entire generative AI lifecycle, from model development to production deployment.

Aethirs provides a decentralized cloud infrastructure that delivers on-demand access to enterprise-grade GPUs for AI model training and real-time gaming applications. This solution addresses the need for scalable, low-latency compute resources while ensuring high performance and security across a global network.

Vultr provides cloud infrastructure with dedicated clusters and on-demand virtual machines powered by AMD and NVIDIA GPUs, enabling efficient deployment of AI and high-performance computing workloads. The platform offers scalable solutions at competitive pricing, addressing the need for accessible and powerful computing resources for developers and businesses globally.

Foundry provides a cloud-native platform offering on-demand access to NVIDIA GPUs across multiple clouds without long-term contracts. The platform integrates with Kubernetes, enabling AI developers to flexibly reserve compute or bid on spot capacity for training and inference workloads. This model allows users to match GPU supply precisely to demand while optimizing costs through usage-based billing and resalable reservations.

Provides a decentralized platform that aggregates idle GPU resources from data centers and independent providers worldwide, creating a scalable and cost-effective infrastructure for on-demand high-performance computing. This system addresses the shortage of AI-grade GPUs by enabling seamless access to thousands of GPUs, including H100s and A6000s, for applications like AI training, rendering, and scientific computation.

Provides a platform for decentralized AI model training by aggregating global compute resources and enabling multi-node GPU deployments across cloud providers. This reduces costs and increases accessibility for developing large-scale models, while allowing contributors to co-own and improve open-source AI innovations.

CoreWeave provides a Kubernetes-native cloud platform optimized for large-scale, GPU-accelerated workloads, offering on-demand access to a diverse range of NVIDIA GPUs and CPU instances. This infrastructure enables businesses to achieve up to 35 times faster performance and 80% cost savings compared to traditional cloud providers, addressing the need for efficient and scalable compute resources in AI and machine learning applications.

XFA AI provides cloud-based GPU rentals through a user-friendly interface, enabling businesses to access high-performance computing resources without the need for significant upfront investment. This service reduces operational costs associated with GPU usage, making advanced computing more accessible for data-intensive applications.

Io.net Cloud offers a decentralized computing network that provides machine learning engineers with instant, permissionless access to global GPU resources for their workloads. This platform enables efficient deployment of pre-configured clusters, significantly reducing costs and deployment time for AI startups.

The startup provides a hybrid cloud platform that combines GPU cloud services with AI controller software for on-premises deployments, enabling enterprises to efficiently manage AI workloads. This solution allows businesses to optimize their data center resources while seamlessly scaling to the public cloud as needed.

Lambda provides on-demand NVIDIA GPU instances and clusters specifically designed for AI training and inference, utilizing advanced hardware like the H100 and GH200 Tensor Core GPUs. This infrastructure enables machine learning teams to efficiently scale their computational resources without long-term commitments, addressing the need for high-performance computing in AI development.

Lambda provides an on‑demand supercomputing platform that lets AI teams provision private, single‑tenant GPU clusters with the latest NVIDIA GB300, B200, and H200 accelerators via a web console or API. The service offers up to 64‑GPU nodes with NVLink and InfiniBand interconnects, SOC 2 Type II security, and pay‑as‑you‑go per‑GPU‑hour billing, enabling scalable training and inference for research labs and enterprise ML teams.

FluidStack provides on-demand access to thousands of NVIDIA A100 and H100 GPUs, enabling AI engineers to rapidly scale their training and inference workloads without long-term contracts. The platform offers fully managed GPU clusters with 24/7 support, significantly reducing operational overhead and accelerating model deployment.

DataCrunch provides on-demand access to high-performance GPU instances and custom-built clusters powered by NVIDIA H200 and H100 technology, enabling efficient model inference and training for machine learning applications. The platform utilizes 100% renewable energy, offering a scalable solution that reduces the infrastructure burden for businesses deploying AI models.

Provides bare-metal access to high-performance AI compute infrastructure powered by NVIDIA HGX H100 GPUs and a 3200 Gbps InfiniBand network, enabling low-latency, scalable training and inference for large-scale machine learning models. Offers transparent pricing and flexible deployment options, including on-demand nodes and long-term contracts, to meet the needs of demanding workloads in AI, HPC, and real-time applications.

Featherless.ai offers serverless AI hosting with a GPU orchestration system, simplifying the deployment and management of AI models. Their platform allows developers to run AI applications without managing underlying infrastructure, optimizing GPU utilization and reducing operational overhead.

Together AI provides a cloud platform that offers serverless OpenAI‑compatible inference APIs for over 200 open‑source models, accelerated up to 4× by its ATLAS runtime. Users can provision on‑demand or reserved NVIDIA GPU clusters for fine‑tuning and batch inference, with per‑token or hourly usage pricing and enterprise‑grade security.

Tensorfuse provides a platform for deploying and managing large language model (LLM) pipelines on cloud infrastructure, allowing users to run serverless GPUs on AWS, Azure, or GCP. The solution enables businesses to scale generative AI models efficiently while keeping data secure within their private cloud, eliminating idle costs and reducing egress charges.

NexGen Cloud provides sustainable Infrastructure as a Service (IaaS) with a focus on high-performance computing (HPC) and GPU infrastructure, utilizing its Hyperstack platform for on-demand GPU as a Service (GPUaaS). The company enables businesses to efficiently integrate AI capabilities into their operations while ensuring data privacy and compliance through its European and North American data centers.

Anyscale provides a configurable AI platform powered by RayTurbo, enabling developers to optimize and scale AI applications across any cloud and hardware configuration. The platform enhances GPU utilization and reduces cloud costs by up to 50%, facilitating faster model training and deployment for complex AI workloads.

Mithril offers an omnicloud platform that aggregates GPU, CPU, and storage resources across multiple cloud providers for AI workloads. It provides algorithmically determined, demand‑driven pricing and a unified interface with batch SDKs and APIs to enable developers to run asynchronous jobs on cost‑effective spot capacity.

KAE Systems offers an Industry Cloud Platform that utilizes containerization and Kubernetes orchestration to provide GPU-based development environments for AI-driven robotics and computer vision applications. This platform enables teams to rapidly deploy and scale their solutions without the need for complex infrastructure, significantly reducing development time and costs.

Mirantis' k0rdent platform automates provisioning of GPU‑optimized, multi‑tenant environments on bare‑metal and Kubernetes, delivering on‑demand AI infrastructure in seconds. It includes a self‑service marketplace, DPU‑based isolation, and a GitOps‑driven control plane for managing hybrid and multi‑cloud clusters, enabling enterprises and service providers to scale AI workloads and monetize idle GPU capacity.

The startup provides AI supercomputers that configure and host GPU servers optimized for deep learning and high-performance computing (HPC) applications. This offering enables organizations to scale their computational resources efficiently while minimizing the infrastructure costs associated with AI workloads.

The startup operates a cloud-based computing platform that provides AI-driven solutions for researchers and enterprises, focusing on large language model development, programmatic data labeling, and machine learning testing. It offers high-performance computing resources, including access to powerful GPUs and virtual machines, while promoting e-waste reduction through environmentally friendly practices.

TensorWave provides a cloud platform optimized for AI workloads, utilizing AMD's Instinct MI300X accelerators for enhanced training, fine-tuning, and inference capabilities. The platform offers immediate availability, lower total cost of ownership, and seamless integration with popular frameworks like PyTorch and TensorFlow, addressing the need for efficient and scalable AI compute solutions.

TensorOpera provides a scalable AI platform that enables developers and enterprises to build and commercialize generative AI applications, utilizing features such as model deployment, serverless GPU cloud processing, and AI agent APIs. Additionally, TensorOpera's FedML platform facilitates secure federated learning across edge devices, allowing organizations to perform decentralized machine learning without compromising data privacy.

Provides a multi-cloud AI compute platform that enables real-time GPU resource management, workload migration, and cost optimization across major cloud vendors. By reducing cluster provisioning times to minutes and supporting multi-node training with fixed budgets, it streamlines AI development and inference while maximizing GPU utilization and reducing operational overhead.

This company provides on-demand GPU cloud infrastructure optimized for AI and machine learning workloads. Their platform offers scalable GPU clusters, high-speed storage, and secure networking, enabling teams to accelerate model training and deployment.

Bach is a platform-as-a-service that automates the setup and management of scalable cloud hosting environments specifically for AI and GPU workloads, eliminating the need for DevOps expertise. By utilizing multi-tenant cluster sharing and auto-scaling, Bach reduces infrastructure costs and accelerates application development, enabling teams to focus on building rather than managing complex cloud systems.

Denvr Cloud provides on-demand and dedicated GPU computing for AI inference and model training, utilizing NVIDIA GPUs and Intel AI accelerators to enhance performance and scalability. The platform simplifies AI operations by offering transparent pricing and real-time cost monitoring, addressing the need for efficient and cost-effective infrastructure in AI development.

Provides a cloud orchestration platform that integrates directly with AWS to deploy and manage AI models and applications on a scalable GPU grid. It eliminates inefficiencies like overprovisioning, idle resources, and reliance on third-party resellers by offering true autoscaling, cost management, and a one-click setup, reducing cloud costs and deployment time significantly.

DODIL provides a unified platform that aggregates SOC‑II‑compliant GPU and CPU capacity from a global network of data centers, delivering high‑performance compute for AI workloads at 60‑70 % lower cost than traditional cloud providers. The service offers managed provisioning, monitoring, auto‑scaling, and raw compute spaces through a web portal and API, simplifying resource allocation and compliance for developers and engineering teams.

The startup provides cloud-based graphics processing services tailored for artificial intelligence research, visual effects production, and data science. By offering scalable computing power for launching AI instances and machine-learning models, it enables organizations to fully utilize their graphics processing capabilities for complex analyses.

Sharon AI provides GPU-driven infrastructure for AI and high-performance computing (HPC) workloads, along with secure, scalable cloud storage solutions. The company enables startups, enterprises, and research institutions to efficiently manage and process large datasets, addressing the growing demand for robust AI capabilities and data management.

VALDI offers on-demand GPU computing power and scalable storage solutions for applications in Generative AI, Machine Learning, and Drug Discovery, utilizing a pay-as-you-go model with no contracts or hidden fees. By providing access to high-performance GPUs like the NVIDIA H100 and A100 at competitive rates, VALDI addresses the need for affordable and flexible computing resources in data-intensive industries.

Morgenrot provides an API‑first GPU cloud marketplace that aggregates idle GPU capacity from multiple data centers into on‑demand compute, with per‑minute billing and automated workload matching. Its GUI‑driven job manager and TailorNode virtualization layer enable real‑time monitoring, fine‑grained GPU slicing, and multi‑tenant GPU‑as‑a‑Service across on‑premise and public‑cloud environments. The platform helps AI data‑center operators and enterprise R&D teams scale GPU resources instantly while improving utilization and controlling costs.

meShare offers a high-performance GPU cloud platform designed for the IoT industry, providing virtualized GPU resources for efficient AI model training and deployment. The platform enables businesses to enhance their operations and product offerings by integrating AI-driven solutions into their processes, significantly reducing costs and improving efficiency.

Hyperstack is a self-service GPU cloud platform that enables users to deploy between 8 and 16,384 NVIDIA H100 SXM GPUs for high-performance computing, machine learning, and data analytics. The platform addresses the need for scalable, cost-effective GPU resources, allowing businesses to efficiently manage demanding workloads without the constraints of traditional cloud providers.

Silicon offers a decentralized marketplace for accessing and renting GPU compute power, addressing the scarcity of specialized AI hardware. The platform connects GPU owners with AI developers, enabling the monetization of idle hardware and providing scalable compute resources for model training and inference.

Jetify Devspace provides a cloud development environment optimized for building AI applications, featuring GPU-enabled instances and automated setup with Devbox for seamless package management. It allows teams to quickly launch projects with pre-built templates and scale resources efficiently, addressing the need for accessible and powerful infrastructure in AI development.

Inceptron provides a unified inference platform that compiles AI model graphs into optimized GPU binaries with automatic operator fusion and hardware‑aware code generation. The managed runtime offers serverless, autoscaling GPU replicas across multiple clouds, integrated MLOps hooks, and built‑in observability and security controls, enabling low‑latency, cost‑effective production inference. Usage is billed per token for serverless deployments or hourly for dedicated GPUs.