Best AI Infrastructure Platforms for Apache Spark
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Compare the Top AI Infrastructure Platforms that integrate with Apache Spark as of August 2025
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This a list of AI Infrastructure platforms that integrate with Apache Spark. Use the filters on the left to add additional filters for products that have integrations with Apache Spark. View the products that work with Apache Spark in the table below.
What are AI Infrastructure Platforms for Apache Spark?
An AI infrastructure platform is a system that provides infrastructure, compute, tools, and components for the development, training, testing, deployment, and maintenance of artificial intelligence models and applications. It usually features automated model building pipelines, support for large data sets, integration with popular software development environments, tools for distributed training stacks, and the ability to access cloud APIs. By leveraging such an infrastructure platform, developers can easily create end-to-end solutions where data can be collected efficiently and models can be quickly trained in parallel on distributed hardware. The use of such platforms enables a fast development cycle that helps companies get their products to market quickly. Compare and read user reviews of the best AI Infrastructure platforms for Apache Spark currently available using the table below. This list is updated regularly.
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1
Vertex AI
Google
Vertex AI provides a robust and scalable AI Infrastructure that supports the development, training, and deployment of machine learning models across a variety of industries. With powerful computing resources and high-performance storage solutions, businesses can efficiently process and manage large datasets for complex AI applications. The platform allows users to scale their AI operations as needed, whether they are training models on smaller datasets or handling large-scale production workloads. New customers get $300 in free credits, which gives them the opportunity to test the platform's infrastructure capabilities without upfront costs. Vertex AI’s infrastructure enables businesses to run their AI applications with speed and reliability, providing the foundation for large-scale deployment of machine learning models.Starting Price: Free ($300 in free credits) -
2
Azure Data Science Virtual Machines
Microsoft
DSVMs are Azure Virtual Machine images, pre-installed, configured and tested with several popular tools that are commonly used for data analytics, machine learning and AI training. Consistent setup across team, promote sharing and collaboration, Azure scale and management, Near-Zero Setup, full cloud-based desktop for data science. Quick, Low friction startup for one to many classroom scenarios and online courses. Ability to run analytics on all Azure hardware configurations with vertical and horizontal scaling. Pay only for what you use, when you use it. Readily available GPU clusters with Deep Learning tools already pre-configured. Examples, templates and sample notebooks built or tested by Microsoft are provided on the VMs to enable easy onboarding to the various tools and capabilities such as Neural Networks (PYTorch, Tensorflow, etc.), Data Wrangling, R, Python, Julia, and SQL Server.Starting Price: $0.005 -
3
BentoML
BentoML
Serve your ML model in any cloud in minutes. Unified model packaging format enabling both online and offline serving on any platform. 100x the throughput of your regular flask-based model server, thanks to our advanced micro-batching mechanism. Deliver high-quality prediction services that speak the DevOps language and integrate perfectly with common infrastructure tools. Unified format for deployment. High-performance model serving. DevOps best practices baked in. The service uses the BERT model trained with the TensorFlow framework to predict movie reviews' sentiment. DevOps-free BentoML workflow, from prediction service registry, deployment automation, to endpoint monitoring, all configured automatically for your team. A solid foundation for running serious ML workloads in production. Keep all your team's models, deployments, and changes highly visible and control access via SSO, RBAC, client authentication, and auditing logs.Starting Price: Free -
4
Vertex AI Notebooks
Google
Vertex AI Notebooks is a fully managed, scalable solution from Google Cloud that accelerates machine learning (ML) development. It provides a seamless, interactive environment for data scientists and developers to explore data, prototype models, and collaborate in real-time. With integration into Google Cloud’s vast data and ML tools, Vertex AI Notebooks supports rapid prototyping, automated workflows, and deployment, making it easier to scale ML operations. The platform’s support for both Colab Enterprise and Vertex AI Workbench ensures a flexible and secure environment for diverse enterprise needs.Starting Price: $10 per GB -
5
Intel Tiber AI Studio
Intel
Intel® Tiber™ AI Studio is a comprehensive machine learning operating system that unifies and simplifies the AI development process. The platform supports a wide range of AI workloads, providing a hybrid and multi-cloud infrastructure that accelerates ML pipeline development, model training, and deployment. With its native Kubernetes orchestration and meta-scheduler, Tiber™ AI Studio offers complete flexibility in managing on-prem and cloud resources. Its scalable MLOps solution enables data scientists to easily experiment, collaborate, and automate their ML workflows while ensuring efficient and cost-effective utilization of resources. -
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E2E Cloud
E2E Networks
E2E Cloud provides advanced cloud solutions tailored for AI and machine learning workloads. We offer access to cutting-edge NVIDIA GPUs, including H200, H100, A100, L40S, and L4, enabling businesses to efficiently run AI/ML applications. Our services encompass GPU-intensive cloud computing, AI/ML platforms like TIR built on Jupyter Notebook, Linux and Windows cloud solutions, storage cloud with automated backups, and cloud solutions with pre-installed frameworks. E2E Networks emphasizes a high-value, top-performance infrastructure, boasting a 90% cost reduction in monthly cloud bills for clients. Our multi-region cloud is designed for performance, reliability, resilience, and security, serving over 15,000 clients. Additional features include block storage, load balancers, object storage, one-click deployment, database-as-a-service, API & CLI access, and a content delivery network.Starting Price: $0.012 per hour -
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NVIDIA RAPIDS
NVIDIA
The RAPIDS suite of software libraries, built on CUDA-X AI, gives you the freedom to execute end-to-end data science and analytics pipelines entirely on GPUs. It relies on NVIDIA® CUDA® primitives for low-level compute optimization, but exposes that GPU parallelism and high-bandwidth memory speed through user-friendly Python interfaces. RAPIDS also focuses on common data preparation tasks for analytics and data science. This includes a familiar DataFrame API that integrates with a variety of machine learning algorithms for end-to-end pipeline accelerations without paying typical serialization costs. RAPIDS also includes support for multi-node, multi-GPU deployments, enabling vastly accelerated processing and training on much larger dataset sizes. Accelerate your Python data science toolchain with minimal code changes and no new tools to learn. Increase machine learning model accuracy by iterating on models faster and deploying them more frequently.
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