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DataRobot vs UiPath AI Center comparison

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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

DataRobot
Ranking in AI Development Platforms
10th
Average Rating
8.0
Reviews Sentiment
7.2
Number of Reviews
10
Ranking in other categories
Predictive Analytics (5th), AIOps (12th), AI Observability (21st), AI Finance & Accounting (6th)
UiPath AI Center
Ranking in AI Development Platforms
23rd
Average Rating
8.6
Reviews Sentiment
4.9
Number of Reviews
2
Ranking in other categories
No ranking in other categories
 

Featured Reviews

Nishant Chauhan - PeerSpot reviewer
Senior Data Engineer at LTM
Accelerated production models have transformed fraud detection and streamlined compliant AI workflows
There are three additional things I would like to add about DataRobot. First, it is not magic; the saying 'garbage in, garbage out' still applies. If your data is messy, has leaks, or the wrong target, DataRobot will just build a bad model faster. It is important to spend time on data prep. Second, free alternatives exist; if the budget is tight, H2O.ai, AutoGluon by AWS, and PyCaret in Python do similar AutoML. DataRobot wins on MLOps with enterprise support, but open-source options win on cost and control. Finally, if you need deep learning for images and text or want full control over every model detail, coding it yourself in Python, TensorFlow, or PyTorch is still better. DataRobot is best for tabular data with business predictions. When it comes to improving DataRobot, I see a few functionalities that need attention. First, the pricing with access is a concern. Enterprise pricing starts at approximately $100,000 per year, which means startups, students, and small teams can't even test it. An improvement would be a real tier, like a $500 per month startup plan. Alternatives like AutoGluon and H2O.ai win here because anyone can try them. Currently, DataRobot operates on a try before you buy basis, which leads to a sales call rather than offering direct sign-up. The second improvement would focus on control versus AutoML trade-offs; while AutoML is fast, sometimes you need to tweak something in preprocessing, but DataRobot hides a lot under the hood. The suggested improvement would allow more granular control without leaving the UI, letting power users directly edit the blueprint code. I would like the ability to change one line instead of rebuilding the whole thing.
reviewer2894310 - PeerSpot reviewer
Software Consultant at a outsourcing company with 201-500 employees
Automated invoice workflows have transformed our document processing and reduced manual effort
UiPath AI Center offers several great features, including the ability to create datasets manually and automate the pipeline for retraining ML models. I can easily set a default timing or create a recurring pipeline to train the documents from Action Center, which provides me with a variety of features, with the easy user interface being one of the best aspects.The recurring pipeline scheduling has saved me a lot of time because I usually have to manually train and mark documents, but when users utilize my bot, they contribute to the data, marking approximately 50% of invoice extraction fields from Action Center. This allows the existing marked data to be automatically retrained, significantly saving me time as a developer and improving the training efficiency since multiple users are validating the invoices, generating a lot of data for training. UiPath AI Center has positively impacted my organization, particularly in automating the invoice extraction process, leading to significant cost savings and time reduction. Manually reading and posting invoices in SAP involves considerable time and effort, but by automating invoice extraction and posting, we now only require users to flag low-confidence fields, allowing for major cycle time reduction and cost savings in invoice processing. I estimate a 50 to 70% reduction in cycle time and roughly a 60% reduction in the required manual effort. Some humans still need to validate the extracted data, so it may be around 60 to 75%.

Quotes from Members

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

Pros

"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."
"By using DataRobot, we save the work equivalent of almost four to five people who are experts in Python and AI, as we can do the same tasks more easily with this tool."
"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."
"Previously we had five or six processes which used to be done manually by different people and that has been transformed using DataRobot because agents now are doing the same thing, resulting in a lot of money saved and around $2 million in cost savings for the bank."
"DataRobot has positively impacted my organization by driving an AI platform that encompasses the entire AI lifecycle, helping us experiment, build, deploy, monitor, and govern AI models in a secure and scalable way."
"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."
"By automating highly technical aspects like model comparison, DataRobot enhances productivity and reduces project timelines from three months to less than one month."
"UiPath AI Center is found to be very valuable because it has improved significantly in extracting information from each invoice."
"UiPath AI Center has positively impacted my organization, particularly in automating the invoice extraction process, leading to significant cost savings and time reduction."
 

Cons

"We dropped the plan to use DataRobot because we found the pricing to be on the higher side."
"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."
"Enterprise pricing starts at approximately $100,000 per year, which means startups, students, and small teams can't even test it."
"Generative AI has taken pace, and I would like to see how DataRobot assists in doing generative AI and large language models."
"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."
"There are some performance issues."
"There is a lack of transparency in the models; sometimes it feels like a black box."
"DataRobot can actually be improved by having access to multiple data repositories. It is lacking in the ways in which it ingests data, in which it transforms the data because we need a separate data manipulation tool for which we need to have somebody else."
"One improvement I would like to see in UiPath AI Center is the addition of more ML models."
"To improve UiPath AI Center, clients need to be convinced to purchase a separate Document Understanding license because it is quite costly."
 

Pricing and Cost Advice

"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."
Information not available
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Top Industries

By visitors reading reviews
Manufacturing Company
13%
Financial Services Firm
12%
Construction Company
9%
University
6%
No data available
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
By reviewers
Company SizeCount
Small Business2
Midsize Enterprise1
Large Enterprise11
No data available
 

Questions from the Community

What is your experience regarding pricing and costs for DataRobot?
Regarding my experience with pricing, setup costs, and licensing for DataRobot, the licensing model does not follow the pay-per-user model typical of SaaS tools. Instead, it is divided into two par...
What needs improvement with DataRobot?
The necessary improvement for DataRobot is its high licensing cost. We also need a robust data infrastructure. For API deployment, we require enhanced data systems, including procuring new servers ...
What is your primary use case for DataRobot?
Our main use case for DataRobot involves predicting SKU across multiple applications and stores, as we have some SKU and unit measurement SKUs where we want to predict our requirements for each sto...
What is your experience regarding pricing and costs for UiPath AI Center?
The pricing for UiPath AI Center is generally acceptable. If pricing could be broken down into simpler parts, such as offering a trial on extracting a specific number of invoices, it would help cli...
What needs improvement with UiPath AI Center?
One improvement I would like to see in UiPath AI Center is the addition of more ML models. Although it currently offers a variety, integrating with Hugging Face to access and deploy their models in...
What is your primary use case for UiPath AI Center?
The major use case I built using UiPath AI Center was for automated invoice extraction, where I created an ML model that extracts all the required fields from invoices. This helped me automate the ...
 

Comparisons

No data available
 

Overview

 

Sample Customers

Harmoney, Zidisha, ONE Marketing, DonorBureau, Trupanion, Avant
Information Not Available
Find out what your peers are saying about DataRobot vs. UiPath AI Center and other solutions. Updated: September 2026.
915,341 professionals have used our research since 2012.