Best AI Development Platforms for Apache Spark
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Compare the Top AI Development Platforms that integrate with Apache Spark as of November 2025
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This a list of AI Development 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 Development Platforms for Apache Spark?
AI development platforms are tools that enable developers to build, manage, and deploy AI applications. These platforms provide the necessary infrastructure for the development of AI models, such as access to data sets and computing resources. They can also help facilitate the integration of data sources or be used to create workflows for managing machine learning algorithms. Finally, these platforms provide an environment for deploying models into production systems so they can be used by end users. Compare and read user reviews of the best AI Development 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 simplifies the process of AI development by providing a fully integrated platform that allows businesses to build, train, and deploy machine learning models with ease. Whether it’s creating models from scratch or customizing pre-trained ones, Vertex AI supports a range of tools that enable developers to experiment and iterate quickly. With an intuitive interface and strong developer support, businesses can accelerate the development of AI-powered applications, enhancing their ability to respond to market demands. New customers receive $300 in free credits, providing the resources needed to explore the wide array of development tools and capabilities available in Vertex AI. This credit helps organizations to prototype and deploy AI models in production, streamlining the development process.Starting Price: Free ($300 in free credits) -
2
Union Cloud
Union.ai
Union.ai is an award-winning, Flyte-based data and ML orchestrator for scalable, reproducible ML pipelines. With Union.ai, you can write your code locally and easily deploy pipelines to remote Kubernetes clusters. “Flyte’s scalability, data lineage, and caching capabilities enable us to train hundreds of models on petabytes of geospatial data, giving us an edge in our business.” — Arno, CTO at Blackshark.ai “With Flyte, we want to give the power back to biologists. We want to stand up something that they can play around with different parameters for their models because not every … parameter is fixed. We want to make sure we are giving them the power to run the analyses.” — Krishna Yeramsetty, Principal Data Scientist at Infinome “Flyte plays a vital role as a key component of Gojek's ML Platform by providing exactly that." — Pradithya Aria Pura, Principal Engineer at GojStarting Price: Free (Flyte) -
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
RazorThink
RazorThink
RZT aiOS offers all of the benefits of a unified artificial intelligence platform and more, because it's not just a platform — it's a comprehensive Operating System that fully connects, manages and unifies all of your AI initiatives. And, AI developers now can do in days what used to take them months, because aiOS process management dramatically increases the productivity of AI teams. This Operating System offers an intuitive environment for AI development, letting you visually build models, explore data, create processing pipelines, run experiments, and view analytics. What's more is that you can do it all even without advanced software engineering skills. -
6
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. -
7
Saagie
Saagie
The Saagie cloud data factory is a turnkey platform that lets you create and manage all your data & AI projects in a single interface, deployable in just a few clicks. Develop your use cases and test your AI models in a secure way with the Saagie data factory. Get your data and AI projects off the ground with a single interface and centralize your teams to make rapid progress. Whatever your maturity level, from your first data project to a data & AI-driven strategy, the Saagie platform is there for you. Simplify your workflows, boost your productivity, and make more informed decisions by unifying your work on a single platform. Transform your raw data into powerful insights by orchestrating your data pipelines. Get quick access to the information you need to make more informed decisions. Simplify the management and scalability of your data and AI infrastructure. Accelerate the time-to-production of your AI, machine learning, and deep learning models. -
8
Databricks Data Intelligence Platform
Databricks
The Databricks Data Intelligence Platform allows your entire organization to use data and AI. It’s built on a lakehouse to provide an open, unified foundation for all data and governance, and is powered by a Data Intelligence Engine that understands the uniqueness of your data. The winners in every industry will be data and AI companies. From ETL to data warehousing to generative AI, Databricks helps you simplify and accelerate your data and AI goals. Databricks combines generative AI with the unification benefits of a lakehouse to power a Data Intelligence Engine that understands the unique semantics of your data. This allows the Databricks Platform to automatically optimize performance and manage infrastructure in ways unique to your business. The Data Intelligence Engine understands your organization’s language, so search and discovery of new data is as easy as asking a question like you would to a coworker. -
9
Determined AI
Determined AI
Distributed training without changing your model code, determined takes care of provisioning machines, networking, data loading, and fault tolerance. Our open source deep learning platform enables you to train models in hours and minutes, not days and weeks. Instead of arduous tasks like manual hyperparameter tuning, re-running faulty jobs, and worrying about hardware resources. Our distributed training implementation outperforms the industry standard, requires no code changes, and is fully integrated with our state-of-the-art training platform. With built-in experiment tracking and visualization, Determined records metrics automatically, makes your ML projects reproducible and allows your team to collaborate more easily. Your researchers will be able to build on the progress of their team and innovate in their domain, instead of fretting over errors and infrastructure. -
10
The Oracle AI Data Platform unifies the complete data-to-insight lifecycle with embedded artificial intelligence, machine learning, and generative capabilities across data stores, analytics, applications, and infrastructure. It supports everything from data ingestion and governance through to feature engineering, model training, and operationalization, enabling organizations to build trusted AI-driven systems at scale. With its integrated architecture, the platform offers native support for vector search, retrieval-augmented generation, and large language models, while enabling secure, auditable access to business data and analytics across enterprise roles. The platform’s analytics layer lets users explore, visualize, and interpret data with AI-powered assistance, where self-service dashboards, natural-language queries, and generative summaries accelerate decision making.
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