Best Infrastructure-as-a-Service (IaaS) Providers for TensorFlow

Compare the Top Infrastructure-as-a-Service (IaaS) Providers that integrate with TensorFlow as of December 2025

This a list of Infrastructure-as-a-Service (IaaS) providers that integrate with TensorFlow. Use the filters on the left to add additional filters for products that have integrations with TensorFlow. View the products that work with TensorFlow in the table below.

What are Infrastructure-as-a-Service (IaaS) Providers for TensorFlow?

Infrastructure-as-a-Service (IaaS) providers offer virtualized computing resources over the internet, allowing businesses to rent IT infrastructure such as servers, storage, and networking on-demand. IaaS platforms eliminate the need for companies to invest in and maintain physical hardware, offering scalability, flexibility, and cost-efficiency. Users can provision and manage virtual machines, storage, and other resources through web-based dashboards or APIs. IaaS is commonly used for hosting websites, running applications, and supporting data analytics or disaster recovery solutions. Major IaaS providers often offer advanced features like load balancing, security services, and automated backups. Compare and read user reviews of the best Infrastructure-as-a-Service (IaaS) providers for TensorFlow currently available using the table below. This list is updated regularly.

  • 1
    RunPod

    RunPod

    RunPod

    RunPod offers a cloud-based platform designed for running AI workloads, focusing on providing scalable, on-demand GPU resources to accelerate machine learning (ML) model training and inference. With its diverse selection of powerful GPUs like the NVIDIA A100, RTX 3090, and H100, RunPod supports a wide range of AI applications, from deep learning to data processing. The platform is designed to minimize startup time, providing near-instant access to GPU pods, and ensures scalability with autoscaling capabilities for real-time AI model deployment. RunPod also offers serverless functionality, job queuing, and real-time analytics, making it an ideal solution for businesses needing flexible, cost-effective GPU resources without the hassle of managing infrastructure.
    Starting Price: $0.40 per hour
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  • 2
    Amazon Elastic Inference
    Amazon Elastic Inference allows you to attach low-cost GPU-powered acceleration to Amazon EC2 and Sagemaker instances or Amazon ECS tasks, to reduce the cost of running deep learning inference by up to 75%. Amazon Elastic Inference supports TensorFlow, Apache MXNet, PyTorch and ONNX models. Inference is the process of making predictions using a trained model. In deep learning applications, inference accounts for up to 90% of total operational costs for two reasons. Firstly, standalone GPU instances are typically designed for model training - not for inference. While training jobs batch process hundreds of data samples in parallel, inference jobs usually process a single input in real time, and thus consume a small amount of GPU compute. This makes standalone GPU inference cost-inefficient. On the other hand, standalone CPU instances are not specialized for matrix operations, and thus are often too slow for deep learning inference.
  • 3
    Database Mart

    Database Mart

    Database Mart

    Database Mart offers a comprehensive suite of server hosting solutions tailored for diverse computing needs. Their VPS hosting provides isolated CPU, memory, and disk resources with full root or admin access, supporting various applications such as database hosting, mail servers, file sharing, SEO tools, and script testing. These VPS plans come with SSD storage, automated backups, and an intuitive control panel, making them ideal for individuals and small businesses seeking cost-effective solutions. For more demanding applications, Database Mart's dedicated servers offer exclusive resources, ensuring superior performance and security. These servers are customizable to support large software systems and high-traffic e-commerce platforms, providing reliability for critical operations. Their GPU servers feature high-performance NVIDIA GPUs, catering to high-performance computing and advanced AI workloads.
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