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DataRobot: A Leader in the 2024 Gartner® Magic Quadrant™ for Data Science and Machine Learning Platforms

June 26, 2024
· 6 min read

In the years since Gartner last released a Magic Quadrant for Data Science and Machine Learning (DSML), the industry has experienced massive shifts. DataRobot has also transformed dramatically from where we began to where we stand today. The rapid pace of AI advancement is unparalleled, and at DataRobot, I’m most proud of our ability to harness these innovations to ensure organizations can leverage them safely, with governance, and for impactful results. 

This commitment to driving value through AI and our continuous product enhancement is why we are thrilled to be recognized as a Leader in the 2024 Gartner Magic Quadrant for DSML Platforms. Positioned in the Leaders Quadrant for the first time marks a significant milestone for DataRobot, which we believe reflects our transformation and growing influence in the market. I also extend my congratulations to the other companies recognized in the Leaders Quadrant—what a recognition!

As one of the industry leaders in this dynamic landscape, this marks the start of a new era for DataRobot. Our journey is defined by ongoing innovation and progression, ensuring that our current offerings are just the beginning of the groundbreaking advancements on the horizon.

Our Journey to the Leaders Quadrant

Gartner evaluates the Magic Quadrant based on a vendor’s ability to execute and completeness of vision. Companies use the Magic Quadrant to shortlist technology vendors, typically focusing on vendors in the Leaders quadrant. 

DataRobot is named a Leader in the Magic Quadrant and we also scored the highest for the Governance Use Case in the Critical Capabilities for Data Science and Machine Learning Platforms, ML Engineering.

Our journey from democratizing AI to a new set of users, to today expanding to become a unified system of intelligence systems, has been transformative. This journey has been propelled by our laser focus on reimagining our user experience for both generative and predictive AI, adding full support for code-first AI practitioners, broad ecosystem integration, and reliable multi-cloud SaaS and hybrid cloud support. 

With each launch in Spring ‘23, Summer ’23, and Fall ‘23, we fortified our product offering. As an end-to-end platform, we provide an extensive range of capabilities, enabling us to deliver enterprise-grade AI-driven solutions. This evolution reflects how our hard work has kept pace with the rapid advancements in the generative AI space, as we believe is evidenced by our 4.6 out of 5 score on Gartner Peer Insights based on 538 reviews as of June 26, 2024.

AI-Centric Approach

Our platform is built on a foundation of advanced AI technologies for practitioners and their related stakeholders. Our customers leverage sophisticated machine learning algorithms to analyze extensive datasets, uncovering insights and patterns that drive smart and prompt decision-making. DataRobot complements the platform with forward deployed customer engineering teams and applied AI experts to accelerate value delivery.

Seamless Collaboration

Our goal is to enable synergy among participants throughout the end-to-end DSML lifecycle, addressing the needs of all stakeholders to integrate ML and generative AI into business processes. AI practitioners can share use cases, manage files, and control versions with CodeSpaces, a persistent file system integrated with Git, providing access to our comprehensive, hosted Notebook developer environment anytime, anywhere. 

We ensure rapid deployment of any AI project – whether built on or off the DataRobot platform – to any endpoint or consumption experience, facilitating smooth transitions from AI developers to operators. Our unified approach to generative and predictive AI  development, governance, and operations streamlines activities for data science teams, IT personnel, and business users.

Cross-Environment Visibility

The DataRobot AI Platform offers AI observability across environments, whether cloud or on-premise, for all your predictive and generative AI use cases. The unified view across projects, teams and infrastructure enhance cross-environmental governance and security for all customer AI assets.  

Business Results

Enterprise Strategy Group (ESG) validated DataRobot’s rapid deployment is up to 83% faster compared to existing tools. They also found that it can offer cost savings of up to 80%, with a predicted ROI ranging from 3.5x to 4.6x, providing the necessary analytics capabilities for organizations looking to productionalize 20 models. Having served over 1000 customers, including many of the Fortune 50, DataRobot understands what it takes to build, govern, and operate AI safely and at scale.

Ranked #1 for Governance Use Case

We built our governance capabilities to help our customers establish rigorous policies and procedures that protect their bottom line. Our governance framework is designed to uphold the highest standards of integrity, accountability, and transparency across all AI operations. We are thrilled to have been ranked the highest, with a 4.1 out of 5 governance score from Gartner for Governance Use Case!

Commitment to Continuous Innovation 

Our continuous innovation efforts are evident in the over 80 new features we have released in generative and predictive AI over the last year. We continue to innovate and invest in  the user experience, offering comprehensive support for both highly technical code-first users, and no-code users. Stay tuned to our “What’s New” page to see what we have in store next. We’re already deep into our next groundbreaking release. 

I have been working in the DSML space for over a decade, and I recognize that we are on the cusp of what AI has to offer. What I look forward to most every day is listening and learning from our customers and partners to safely accelerate innovation and value delivery. It is both a challenge and pleasure to work in such a dynamic environment where no one knows the “right” answer and we get to test our best ideas and see what works. I look forward to an eventful year or two till the next MQ!

And, if you’re curious about all advancements I talked about, I encourage you all to watch the Data Science and Machine Learning Bake-Off video to see how DataRobot took a problem statement and a raw data set and turned it into an end-user application and judge for yourself.

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Gartner, Magic Quadrant for Data Science and Machine Learning Platforms, Afraz Jaffri, Aura Popa, Peter Krensky, Jim Hare, Raghvender Bhati, Maryam Hassanlou, Tong Zhang, June 17, 2024.

Gartner Critical CapabilitiesTM for Data Science and Machine Learning Platforms, Machine Learning (ML) Engineering, Afraz Jaffri, Aura Popa, Peter Krensky, Jim Hare, Tong Zhang, Maryam Hassanlou, Raghvender Bhati, Published June 24, 2024.

GARTNER is a registered trademark and service mark of Gartner, Inc. and/or its affiliates in the U.S. and internationally, and MAGIC QUADRANT and PEER INSIGHTS are registered trademarks of Gartner, Inc. and/or its affiliates and are used herein with permission. All rights reserved.

Gartner does not endorse any vendor, product or service depicted in its research publications, and does not advise technology users to select only those vendors with the highest ratings or other designation. Gartner research publications consist of the opinions of Gartner’s research organization and should not be construed as statements of fact. Gartner disclaims all warranties, expressed or implied, with respect to this research, including any warranties of merchantability or fitness for a particular purpose.

Gartner Peer Insights content consists of the opinions of individual end users based on their own experiences with the vendors listed on the platform, should not be construed as statements of fact, nor do they represent the views of Gartner or its affiliates. Gartner does not endorse any vendor, product or service depicted in this content nor makes any warranties, expressed or implied, with respect to this content, about its accuracy or completeness, including any warranties of merchantability or fitness for a particular purpose.

This graphic was published by Gartner, Inc. as part of a larger research document and should be evaluated in the context of the entire document. The Gartner document is available upon request from DataRobot.

About the author
Venky Veeraraghavan
Venky Veeraraghavan

Chief Product Officer

Venky Veeraraghavan leads the Product Team at DataRobot, where he drives the definition and delivery of DataRobot’s AI platform. Venky has over twenty-five years of experience as a product leader, with previous roles at Microsoft and early-stage startup, Trilogy. Venky has spent over a decade building hyperscale BigData and AI platforms for some of the largest and most complex organizations in the world. He lives, hikes and runs in Seattle, WA with his family.

Meet Venky Veeraraghavan
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