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DataRobot vs Hugging Face comparison

 

Comparison Buyer's Guide

Executive SummaryUpdated on Dec 4, 2024

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
15th
Average Rating
8.2
Reviews Sentiment
7.0
Number of Reviews
6
Ranking in other categories
Predictive Analytics (5th), AIOps (15th), AI Observability (74th), AI Finance & Accounting (6th)
Hugging Face
Ranking in AI Development Platforms
2nd
Average Rating
8.2
Reviews Sentiment
7.2
Number of Reviews
14
Ranking in other categories
No ranking in other categories
 

Mindshare comparison

As of February 2026, in the AI Development Platforms category, the mindshare of DataRobot is 1.9%, up from 1.3% compared to the previous year. The mindshare of Hugging Face is 7.2%, down from 13.2% compared to the previous year. It is calculated based on PeerSpot user engagement data.
AI Development Platforms Market Share Distribution
ProductMarket Share (%)
Hugging Face7.2%
DataRobot1.9%
Other90.9%
AI Development Platforms
 

Featured Reviews

Naqash Ahmed - PeerSpot reviewer
Senior Data Reporting Analyst at University of Bradford
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.
Mihir Jadhav - PeerSpot reviewer
Software Engineer at Futurescape Technologies
Integration of open-source models and deployment in cloud apps has drastically improved productivity
The best features Hugging Face offers are Transformers and Spaces to deploy the app in clicks. What I like most about Transformers and Spaces is the ease of use. Hugging Face is like a Git repository, so it is very helpful and easy to use. Hugging Face has positively impacted my organization because we can deploy open-source applications for testing on Spaces, and one of the main things is the models that it provides and the number of open-source models to compare with. The main part is that it offers inference as well for free for many of the models, so we can use it directly in our applications with a few lines of code.

Quotes from Members

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

Pros

"DataRobot can be easy to use."
"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."
"Tasks such as model testing, feature engineering, and predictions that used to take us days or weeks can now be accomplished in hours."
"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."
"DataRobot is highly automated, allowing data scientists to build models easily."
"By automating highly technical aspects like model comparison, DataRobot enhances productivity and reduces project timelines from three months to less than one month."
"My preferred aspects are natural language processing and question-answering."
"There are numerous libraries available, and the documentation is rich and step-by-step, helping us understand which model to use in particular conditions."
"The tool's most valuable feature is that it shows trending models. All the new models, even Google's demo models, appear at the top. You can find all the open-source models in one place. You can use them directly and easily find their documentation. It's very simple to find documentation and write code. If you want to work with AI and machine learning, Hugging Face is a perfect place to start."
"We have seen improved productivity and time saved from using Hugging Face; for a task that would have taken six hours, it saved us five hours, and we completed it in one hour with the plug-and-play integration of inference and everything, using the few lines of code that Hugging Face provides."
"The tool's most valuable feature is that it's open-source and has hundreds of packages already available. This makes it quite helpful for creating our LLMs."
"It is stable."
"Overall, the platform is excellent."
"What I find the most valuable about Hugging Face is that I can check all the models on it and see which ones have the best performance without using another platform."
 

Cons

"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."
"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."
"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."
"There is a lack of transparency in the models; sometimes it feels like a black box."
"Everything is pretty much sorted in Hugging Face, but it could be improved if there was an AI chatbot or an AI assistant in Hugging Face platform itself, which can guide you through the whole platform, making it easier for the user."
"Implementing a cloud system to showcase historical data would be beneficial."
"I've worked on three projects using Hugging Face, and only once did we encounter a problem with the code. We had to use another open-source embedding from OpenAI to resolve it. Our team has three members: me, my colleague, and a team leader. We looked at the problem and resolved it."
"Most people upload their pre-trained models on Hugging Face, but more details should be added about the models."
"The solution must provide an efficient LLM."
"Access to the models and datasets could be improved."
"The initial setup can be rated as a seven out of ten due to occasional issues during model deployment, which might require adjustments."
"Access to the models and datasets could be improved. Many interesting ones are restricted."
 

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."
"Hugging Face is an open-source solution."
"The solution is open source."
"I recall seeing a fee of nine dollars, and there's also an enterprise option priced at twenty dollars per month."
"The tool is open-source. The cost depends on what task you're doing. If you're using a large language model with around 12 million parameters, it will cost more. On average, Hugging Face is open source so you can download models to your local machine for free. For deployment, you can use any cloud service."
"We do not have to pay for the product."
"So, it's requires expensive machines to open services or open LLM models."
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Top Industries

By visitors reading reviews
Financial Services Firm
13%
Manufacturing Company
12%
Computer Software Company
10%
Educational Organization
8%
University
10%
Comms Service Provider
10%
Manufacturing Company
9%
Financial Services Firm
9%
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
No data available
By reviewers
Company SizeCount
Small Business9
Midsize Enterprise2
Large Enterprise3
 

Questions from the Community

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...
What needs improvement with Hugging Face?
Everything is pretty much sorted in Hugging Face, but it could be improved if there was an AI chatbot or an AI assistant in Hugging Face platform itself, which can guide you through the whole platf...
What is your primary use case for Hugging Face?
My main use case for Hugging Face is to download open-source models and train on a local machine. We use Hugging Face Transformers for simple and fast integration in our applications and AI-based a...
What advice do you have for others considering Hugging Face?
We have seen improved productivity and time saved from using Hugging Face; for a task that would have taken six hours, it saved us five hours, and we completed it in one hour with the plug-and-play...
 

Comparisons

 

Overview

 

Sample Customers

Harmoney, Zidisha, ONE Marketing, DonorBureau, Trupanion, Avant
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Find out what your peers are saying about DataRobot vs. Hugging Face and other solutions. Updated: December 2025.
881,733 professionals have used our research since 2012.