DataRobot Reviews

Name: DataRobot
Brand: DataRobot
Rating: 4.3 (5 reviews)

4.3 out of 5

5 reviews
100% willing to recommend

What is DataRobot?

DataRobot captures the knowledge, experience and best practices of the world’s leading data scientists, delivering unmatched levels of automation and ease-of-use for machine learning initiatives. DataRobot enables users to build and deploy highly accurate machine learning models in a fraction of the time.

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DataRobot is the #5 ranked solution in top Predictive Analytics solutions, #14 ranked solution in top AI Development Platforms, and #15 ranked solution in top AIOps solutions. PeerSpot users give DataRobot an average rating of 8.6 out of 10. DataRobot is most commonly compared to Datadog: DataRobot vs Datadog. DataRobot is popular among the large enterprise segment, accounting for 65% of users researching this solution on PeerSpot. The top industry researching this solution are professionals from a financial services firm, accounting for 15% of all views.

Buyer's Guide

Predictive Analytics

July 2025

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Helped 864,155 peers since 2012

Featured DataRobot reviews

SagarYadav

Staff Specialist Data Scientist at BMC Software, Inc.

DataRobot is equipped with a GUI-based approach that simplifies the process of feature engineering and model training. It provides AutoML capabilities, which allow for comparing thousands of models and selecting the best-suited one based on business requirements. By automating highly technical aspects like model comparison, DataRobot enhances productivity and reduces project timelines from three months to less than one month.

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Raviteja Guna

Data Scientist at a tech services company with 10,001+ employees

Based on your similar requirements, V10.0 has very cool features related to AI Generation. I would suggest the team, too. This is very good for data scientists or people who don't want to code. Even the documentation is well-maintained in terms of its capabilities. It's easy to navigate. The support documents are good. Even someone with basic IT knowledge can easily navigate. If you're an IT engineer, you can efficiently perform operations using it. We have deployed eight to nine use cases on DataRobot and have seen a tremendous response in accuracy and performance. We are pleased because we conducted a comparison. We took a model we built using a sample Python on a local machine and applied the same data and process using DataRobot Autopilot. The results were pretty amazing, with promising accuracy and recall. The accessibility is so easy. Even a college graduate with essential experience can use it. Suppose I do the same model in Databricks and want to monitor my MLOps pipeline. So, I need to use a third-party framework again, like MLflow, Kubeflow, Airflow, or whatever. I need to build my dashboards and everything, customization dashboards. However, everything is available in DataRobot. I can use it directly. They have a new option called DataRobot apps. So, on the predictions, we can even create customized apps. I can build my dashboard, and I can develop my applications. Overall, I rate the solution an eight out of ten.

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RangeshKunnavakkam

Consultant at Netsoft

I used DataRobot extensively at DISH Wireless (my previous company). It's very user-friendly, and the data munching is fast. They have another product that helps with data processing as well. It connects well with Snowflake, which is pretty fast as an engine. Different models, especially financial ones, run very fast in DataRobot. They have a strong focus on financial applications.

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DataRobot mindshare

Product category:

As of August 2025, the mindshare of DataRobot in the Predictive Analytics category stands at 8.9%, up from 8.3% compared to the previous year, according to calculations based on PeerSpot user engagement data.

Predictive Analytics

Key learnings from peers

Valuable Features

DataRobot is easy to use and automates MLOps, enabling efficient management of models and logs. It simplifies feature engineering and model training with its GUI-based approach and AutoML capabilities, comparing thousands of models effectively. It facilitates end-to-end processes, making model building and deployment seamless for data scientists. Highly valued for its automation, it reduces project timelines significantly, enhancing productivity by addressing various data science challenges.

"By automating highly technical aspects like model comparison, DataRobot enhances productivity and reduces project timelines from three months to less than one month."
"DataRobot is highly automated, allowing data scientists to build models easily."
"It's easy to do MLOps operations. It's a lot easier to manage jobs and see the logs if there's any drift in a model."

Room for Improvement

DataRobot users suggest enhancing integration by allowing existing Python or R code integration and adding generative AI and large language model capabilities. Performance improvement and storage-related service enhancements are needed, with better integration with orchestration tools for MLOps. Users express a desire for more features beyond the UI-based interface, indicating a need for functionality improvements.

"DataRobot is a UI-based tool, which means it cannot provide all the features I might manually implement through notebooks or Python. In this aspect, I see room for improvement in its functionality."
"There are some performance issues."
"Generative AI has taken pace, and I would like to see how DataRobot assists in doing generative AI and large language models."

ROI

Users frequently note that DataRobot significantly improved predictive accuracy, streamlined workflows, and reduced manual tasks. Many experienced substantial cost savings and increased revenue. Time-to-market for new models shortened notably. DataRobot's automated machine learning capabilities led to better resource allocation and higher productivity. Some mentioned rapid integration into existing systems, enhancing efficiency. Decision-making processes were more data-driven and strategic. Many stated that its scalability addressed various business requirements effectively.

Pricing

DataRobot offers flexible pricing models, typically customized based on enterprise needs and usage. Users report satisfaction with the ROI and appreciate the scalable options. However, some indicate that costs can be high, especially for smaller teams, and suggest evaluating all features versus actual requirements. Enterprise buyers should consider both short-term and long-term costs when evaluating DataRobot.

"The price of DataRobot is good because if you take the price of the solution which is approximately $65,000, it is less than a data scientist. There are very few data scientists available."
"We dropped the plan to use DataRobot, because we found the pricing to be on the higher sise. We liked DataRobot a lot, but due to the pricing, we dropped that idea."

Popular Use Cases

Healthcare startups utilize DataRobot for developing robotic process automation in the insurance and hospital sectors with machine learning. They leverage it for model creation, monitoring as MLOps, feature pre-processing, and data science capabilities. Technology firms implement it for rapid development via GUI integration with datasets for feature engineering, prediction modeling, and deployment. They conduct POCs to assess its effectiveness, often preferring cloud deployment, though specific data sometimes remains on-premise.

Service and Support

Users indicate that DataRobot's customer service is responsive and efficient. They appreciate timely resolutions and knowledgeable support staff. Users also mention that the support team provides thorough assistance, ensuring issues are addressed promptly. The service is noted for being approachable, with a positive attitude towards solving customer problems.

Deployment

Users describe DataRobot's initial setup as straightforward and quick, often completed within hours. They appreciate detailed documentation and helpful support. Some mention minor challenges with configuration but find the overall process intuitive. Feedback highlights the ease of integrating with existing systems and the minimal technical expertise required. Users also like how DataRobot's installation guides simplify the deployment, making the onboarding process efficient and user-friendly.

Scalability

DataRobot is praised for its impressive scalability, enabling seamless handling of increasing data volumes. Users find it well-suited for managing large datasets without significant performance drops. Its architecture supports both small scale and extensive enterprise-wide deployments efficiently. Many appreciate its automated processes, which minimize manual intervention, and its capacity to integrate with various data sources, making it a versatile choice for expanding business demands.

Stability

DataRobot is widely regarded as stable according to user feedback. While some users had limited interaction, they still observed its stability. Users deploying edge analytics appreciated its reliability and praised its standing among top AI offerings. No major stability issues were noted, though enhancements addressed minor concerns like duplicate projects. Stability was a consistent highlight in discussions, pointing to DataRobot's dependable performance.

These insights are based on the in-depth reviews provided by peers to help you make a better buying decision.

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