Data science and machine learning platforms are integrated environments combining code-based libraries and low-code tools to support the full data science lifecycle, from data preparation and modeling to deployment and governance. Accessible via desktop or cloud, these platforms facilitate collaboration across data scientists, IT and business users, enabling the development of predictive and generative AI applications using structured and unstructured data types such as text, images and video.
DSML platforms accelerate insight generation and model deployment while promoting reuse, consistency and collaboration. They support a range of techniques, including regression, deep learning, reinforcement learning and GenAI. Integrated MLOps tools manage workflows across environments, while low-code and natural language interfaces allow broader user participation. The result is faster development, easier scaling and better integration of AI into business processes.
Essential features include data access, transformation tools, code and low-code development, model evaluation, deployment and life cycle management. Advanced capabilities span AutoML, GPU support, explainability tools, hyperparameter tuning, metadata management and GenAI functionality. Together, these features enable flexible, scalable and transparent AI solutions across enterprises.
Discover which companies are leaders in this magic quadrant and what their pros and cons are below.
Databricks: Data Intelligence Platform
Databricks offers an integrated Data Intelligence Platform with lakehouse, data engineering, governance, analytics and GenAI capabilities. It serves midsize and large enterprises globally. In 2024, it raised $10 billion in Series J funding and acquired Tabular, Lilac and Prodvana to enhance data compatibility, GenAI and infrastructure. In 2025, it partnered with SAP to integrate its platform into the SAP Business Data Cloud.
Strengths: Vision, Leadership & Practitioner Popularity
Databricks has a comprehensive, agent-based ecosystem that integrates data engineering, machine learning and generative AI, with assistive analytics and AI development features tailored to high-stakes use cases like financial and reputational risk. Its leadership team has remained stable since inception, consistently making strategic decisions around acquisitions and emerging technologies. The platform has also gained strong popularity among data scientists and ML engineers, boosting the availability of skilled practitioners in the market.
Weakness: Learning Curve, Competition and Infrastructure
Databricks users often face a steep learning curve, as effective platform use requires significant experience and skill development. While Databricks once led as the only integrated lakehouse and DSML platform, it now faces growing competition from other vendors offering similar capabilities. Additionally, some native cloud provider features—such as GPU serverless compute for ML and support for custom processors—may not be available within the Databricks environment.
Amazon Web Services: SageMaker AI & Bedrock
Amazon Web Services (AWS) uses Amazon SageMaker AI and Amazon Bed2rock to provide comprehensive AI infrastructure, tools and governance for building and deploying models and generative AI applications. Serving organizations of all sizes across diverse regions and sectors, AWS announced in December 2024 the next generation of Amazon SageMaker, featuring a Unified Studio that integrates data storage, processing, analytics, DSML model building and GenAI development. It also introduced new Amazon Nova models capable of processing text, images and video as prompts.
Strengths: Collaboration, AI App Ecosystem & Responsible AI
SageMaker Unified Studio offers a fully integrated environment that combines data, analytics and AI to streamline collaboration and enhance project visibility. Its AI app ecosystem supports third-party SageMaker Partner AI apps, allowing providers to deliver specialized solutions without managing infrastructure. Additionally, AWS emphasizes responsible AI through Amazon Bedrock Guardrails, which include features like Automated Reasoning checks, and has earned ISO accreditation for Bedrock.
Weaknesses: Integration and Flexibility, Foundation Models and FinOps
As a new offering, SageMaker Unified Studio faces challenges with integration across other cloud platforms. While AWS provides a growing selection of capable foundation models, they have yet to become a key factor in platform selection for many customers. Additionally, managing and forecasting AI infrastructure costs remains a challenge on AWS, making FinOps a complex aspect of platform usage.
Google: Vertex AI Platform
Google’s Vertex AI platform offers enterprise teams a comprehensive suite of tools for data and AI governance, engineering, analysis, data science, MLOps and GenAI and AI agent development, all built on Google Cloud infrastructure. Serving enterprises of all sizes across diverse industries, excluding China. Google continues to advance GenAI through its Gemini foundation models developed by Google DeepMind. In March 2025, it introduced Gemini 2.5 Pro, expanding its 2.0 model lineup, which includes Pro, Flash, Flash-Lite and Flash Thinking.
Strengths: Unified Governance, RAG & Co-innovation
Google enhances Vertex AI with unified governance through Dataplex, which captures, stores and exposes all artifacts related to datasets, models and features. Vertex AI Search supports retrieval-augmented generation (RAG), enabling GenAI apps and agents to operate across structured and unstructured data, with grounding from Google Search, enterprise data and trusted third-party sources. Additionally, Google collaborates with clients through dedicated AI engineering teams that apply advanced research to real-world industry challenges.
Weaknesses: Community, Complexity and Coherence
Vertex AI faces challenges with limited community support compared to other providers, potentially leading to longer issue resolution times. Its core features can be complex and may require additional abstraction layers to improve usability for data scientists and developers. Additionally, the multiple RAG-based tools—such as Vertex AI Search, Vertex AI RAG Engine and Vector Search—may create confusion when navigating the platform’s offerings.
Microsoft: Azure AI Foundry
Microsoft’s Azure Machine Learning (Azure ML) platform provides data scientists, AI engineers, and pro-code developers with tools for model development, training, AutoML and MLOps, along with integrated governance, security, compute resources and custom AI infrastructure. Serving enterprises of all sizes across diverse regions and sectors, Microsoft expanded its AI portfolio in November 2024 with the launch of Azure AI Foundry, a new offering that brings existing and new services together, including Azure ML, for building and deploying AI- and agent-based solutions.
Strengths: Innovation, Ecosystem & Pricing
Microsoft supports innovation through Azure AI Foundry Labs, which enables safe experimentation with leading AI frameworks. Its extensive ecosystem includes partnerships with numerous third-party platforms, allowing for best-of-breed integrations and unified platform use. Additionally, Microsoft offers flexible pricing for generative AI and broad geographic coverage, making its AI solutions accessible to organizations of all sizes.
Weaknesses: GenAI Models, Copilot and Product Strategy
Azure AI Foundry hosts models from multiple providers, allowing buyers to explore alternatives as performance differences with OpenAI models narrow. Microsoft Copilot supports data science tasks such as exploratory analysis and model building, though its availability is currently limited to notebooks. However, Microsoft’s evolving AI product branding, including the shift to Azure AI Foundry, has led to confusion among buyers and users of Azure ML.
Dataiku: LLM Guard Services
Dataiku’s platform blends low-code, AI-assisted tools with code-first interfaces like notebooks and code editors to support enterprise-wide collaboration and AI application development. Serving midsize and large enterprises across all sectors with geographically diversified operations, Dataiku advanced its generative AI capabilities in October 2024 by expanding its LLM Mesh feature with the LLM Guard Services suite, designed to help enterprises move GenAI projects from proof of concept to production with built-in monitoring for cost, quality and safety.
Strengths: Core Data Science, Customer Support & Market Understanding
Dataiku focuses on insight-driven data science while expanding its GenAI capabilities, including its Stories feature to enhance decision-making. Its services team receives consistently high customer praise for support during setup and initial use cases. With LLM Mesh, Dataiku addresses key GenAI development challenges such as governance and model consistency, reflecting a deep understanding of market needs.
Weaknesses: Feature Delivery, Buyer Awareness and Differentiation
As Dataiku expands its capabilities, it faces the risk of not delivering best-in-class features across all aspects of data science and AI. Many enterprises use only select platform parts to address specific needs, which can limit broader investment. Additionally, because Dataiku operates on top of other vendors in this Magic Quadrant, many of which offer overlapping functionality, its differentiation in the market can be challenging.
DataRobot: Enterprise AI
DataRobot’s Enterprise AI Suite is a platform that enables the development of predictive and generative AI solutions with no-code interfaces for business users and coding environments for expert data scientists. Operating globally, except in China, DataRobot serves enterprises of various sizes and sectors. At the end of 2024, the company refreshed its brand and positioning to emphasize delivering, managing and monitoring AI applications through models and agents across multiple horizontal and vertical domains.
Strengths: Vision, Acquisitions & Marketing Understanding
DataRobot’s vision to create an AI-driven business app ecosystem with agent orchestration aligns well with enterprise needs for building agentic systems. Its acquisition of Agnostiq, including the open-source distributed computing platform Covalent, supports this strategic direction. Additionally, DataRobot’s Unmet AI Needs Survey demonstrates its commitment to understanding the challenges enterprises face in leading AI adoption.
Weaknesses: Community, User Experience and Partnerships
Despite rebranding, DataRobot is still perceived as less suited for expert data science, which may hinder the growth of a strong practitioner community. Frequent platform updates have also complicated the user experience, making low-code and pro-code interactions harder to navigate. Furthermore, building a robust business ecosystem will require securing commitment from application vendors to ensure integration and support.
IBM: Watsonx
IBM’s watsonx platform supports the development of business AI applications through integrated data management, governance and predictive and generative AI tools for data scientists and AI engineers. Serving enterprises of all sizes across diverse regions, IBM has introduced several innovations over the past year to enable multi-agent systems, including the watsonx Orchestrate platform, a partnership with CrewAI to streamline agentic workflows, and the open-source BeeAI Framework. In February 2025, IBM also announced plans to acquire DataStax to enhance its generative AI and data management capabilities.
Strengths: Flexibility, AI-ready Data & Innovation
IBM offers a flexible AI platform with a broad range of tools, foundation models, GPU options and open-source and proprietary frameworks to meet diverse enterprise needs. Its planned acquisition of DataStax will enhance its ability to deliver RAG-based solutions, while continued innovation from IBM Research is reflected in differentiated offerings like the Granite model family, AutoRAG and InstructLab.
Weaknesses: Awareness, Core Data Science and Multicloud
IBM faces challenges in market awareness, as the performance and capabilities of its foundation models and AI innovations are less recognized compared to providers with more consumer-facing products. Additionally, SPSS is not prominently integrated into watsonx for core data science tasks, limiting its visibility for such use cases. watsonx also lacks the same depth of multicloud integration with Google Cloud Platform as it has with AWS and Azure.
Altair: RapidMiner
Altair’s RapidMiner platform and related products enable enterprises to build AI-driven automation solutions across diverse environments. Serving primarily midsize to large clients in industries such as automotive, aerospace, defense and healthcare, Altair operates globally. In early 2024, it acquired Cambridge Semantics, integrating its knowledge graph and analytical database technology into Altair’s DSML platform as an “AI fabric” offering. This analysis also reflects Altair’s status following its March 2025 acquisition by Siemens.
Strengths: Market Understanding, Acquisitions & Customer Engagement
Altair demonstrates strong market understanding through its differentiated AI fabric vision, which unifies AI agents, data fabric and AI engineering at scale. Its targeted acquisitions, including Cambridge Semantics and a regional services provider in Asia/Pacific, align with this strategy and expand its global footprint. Additionally, Altair supports customer success through its AI Centre of Excellence, offering a structured implementation program that promotes effective deployment without creating long-term dependencies.
Weaknesses: Viability, Customer Awareness and Product Portfolio
Altair’s recent acquisition by Siemens introduces uncertainty about its future direction and strategic focus. Additionally, the AI fabric concept requires clearer communication to increase visibility and consideration among potential DSML platform buyers. The breadth of Altair’s product portfolio, spanning within and beyond analytics and AI, also contributes to integration challenges and confusion for current and prospective customers.
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