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Data Hub vs DataRobot comparison

 

Comparison Buyer's Guide

Executive Summary

Review summaries and opinions

We asked business professionals to review the solutions they use. Here are some excerpts of what they said:
 

Categories and Ranking

Data Hub
Ranking in AI Observability
12th
Average Rating
9.4
Reviews Sentiment
2.2
Number of Reviews
3
Ranking in other categories
Metadata Management (7th)
DataRobot
Ranking in AI Observability
66th
Average Rating
8.2
Reviews Sentiment
7.0
Number of Reviews
6
Ranking in other categories
Predictive Analytics (5th), AI Development Platforms (15th), AIOps (15th), AI Finance & Accounting (4th)
 

Featured Reviews

reviewer2784462 - PeerSpot reviewer
Software Engineer at a tech vendor with 10,001+ employees
Centralized metadata has empowered governed data discovery and clarified ownership for all teams
The impact is very positive, and there are many benefits for us using Data Hub because it was easier to make data governance, create centralized metadata management, improve data discoverability, and manage data in general. The areas for improvement, in my opinion, are the initial setup and configuration that can be complex without prior experience, especially in large-scale environments. User experience for non-technical users could be further simplified, particularly around advanced metadata concepts. The out-of-the-box governance workflow, for example, approvals and certification, could be more prescriptive for customers at early maturity stages. Data Hub can be improved in the initial setup and configuration that is somewhat complex, and also in operational monitoring that could benefit from more native dashboards and alerts. However, these are not blockers, but areas where additional guidance or product enhancement would further accelerate adoption.
Naqash Ahmed - PeerSpot reviewer
Senior Data Reporting Analyst at a educational organization with 1,001-5,000 employees
Automation has improved efficiency and decision-making while big data handling and transparency still need work
Aside from the many advantages of DataRobot, I believe there are areas that could be improved based on my experience. There is a lack of transparency in the models; sometimes it feels like a black box. For example, when I uploaded a large data set of about two gigabytes for processing, the time taken was slower than expected. Additionally, the handling of bigger data sets could be better, as it performs extremely well with smaller datasets but can lag with larger ones. The integration with some other tools used in our organization can also be challenging, and more flexibility for custom pre-processing and advanced model tuning would be beneficial. In terms of support and documentation, I believe improvements are needed. For instance, the response time from DataRobot could be quicker, which would be appreciated when we need assistance. The documentation is generally sufficient, but it can be lengthy and could use more real-world examples and step-by-step tutorials for better clarity. Lastly, creating a client community where users can share experiences and solutions might enhance the overall value and learning curve.

Quotes from Members

We asked business professionals to review the solutions they use. Here are some excerpts of what they said:
 

Pros

"Data Hub proved to be a robust, scalable, enterprise-ready data catalog that is well-suited for AWS-based architecture and complex organizational environments."
"We especially like the initial part of feature engineering, because feature engineering is included in most engines, but DataRobot has an excellent way of picking up the right features."
"By automating highly technical aspects like model comparison, DataRobot enhances productivity and reduces project timelines from three months to less than one month."
"Tasks such as model testing, feature engineering, and predictions that used to take us days or weeks can now be accomplished in hours."
"DataRobot can be easy to use."
"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."
 

Cons

"The areas for improvement, in my opinion, are the initial setup and configuration that can be complex without prior experience, especially in large-scale environments."
"There are some performance issues."
"There is a lack of transparency in the models; sometimes it feels like a black box."
"If we could include our existing Python or R code in DataRobot, we could make it even better. The DataRobot that we have is specific to an industry, but most of the time we would have our own algorithms, which are specific to our own use case. If we had a way by which we could integrate our proprietary things into DataRobot with a simple integration, it would help us a lot."
"Generative AI has taken pace, and I would like to see how DataRobot assists in doing generative AI and large language models."
"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."
"The business departments will love to work with DataRobot because they use the tool to investigate their data, such as targeting what they want to investigate. They don't need any data scientists near them. They can investigate at eye level and bring into the BI tool, or can bring it to the data scientist. Data scientists can use this tool to bring increase the solution to the maximum. All the others can use it, but not to the maximum."
 

Pricing and Cost Advice

Information not 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."
"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."
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Top Industries

By visitors reading reviews
No data available
Financial Services Firm
14%
Manufacturing Company
12%
Computer Software Company
10%
Retailer
8%
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
No data available
No data available
 

Questions from the Community

What needs improvement with Data Hub?
The impact is very positive, and there are many benefits for us using Data Hub because it was easier to make data governance, create centralized metadata management, improve data discoverability, a...
What is your primary use case for Data Hub?
We adopted Data Hub in the context of a large enterprise customer operating in a regulated industry with a strong focus on data governance, data discoverability, and ownership clarity across multip...
What advice do you have for others considering Data Hub?
Based on internal measurement and feedback from the data teams, there are many impacts. Time to locate and understand a data set was reduced by approximately 40-50 percent. Manual documentation eff...
What is your experience regarding pricing and costs for DataRobot?
While pricing falls more under my IT colleagues, from my perspective, the overall experience feels justified. The premium pricing is reasonable for the value provided, and I'd say it's worth the in...
What needs improvement with DataRobot?
Aside from the many advantages of DataRobot, I believe there are areas that could be improved based on my experience. There is a lack of transparency in the models; sometimes it feels like a black ...
What is your primary use case for DataRobot?
My main use case for DataRobot is to perform predictive analysis and automation of machine learning workflows. I use it to quickly build, test, and deploy models without extensive coding. One of th...
 

Also Known As

Acryl Data
No data available
 

Overview

 

Sample Customers

Information Not Available
Harmoney, Zidisha, ONE Marketing, DonorBureau, Trupanion, Avant
Find out what your peers are saying about Data Hub vs. DataRobot and other solutions. Updated: December 2025.
881,114 professionals have used our research since 2012.