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Hugging Face vs Microsoft Azure Machine Learning Studio 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

Hugging Face
Ranking in AI Development Platforms
4th
Average Rating
8.2
Reviews Sentiment
7.0
Number of Reviews
12
Ranking in other categories
No ranking in other categories
Microsoft Azure Machine Lea...
Ranking in AI Development Platforms
3rd
Average Rating
7.6
Reviews Sentiment
7.0
Number of Reviews
61
Ranking in other categories
Data Science Platforms (4th)
 

Mindshare comparison

As of April 2025, in the AI Development Platforms category, the mindshare of Hugging Face is 13.5%, up from 7.6% compared to the previous year. The mindshare of Microsoft Azure Machine Learning Studio is 7.3%, down from 13.7% compared to the previous year. It is calculated based on PeerSpot user engagement data.
AI Development Platforms
 

Featured Reviews

SwaminathanSubramanian - PeerSpot reviewer
Versatility empowers AI concept development despite the multi-GPU challenge
Regarding scalability, I'm finding the multi-GPU aspect of it challenging. Training the model is another hurdle, although I'm only getting into that aspect currently. Organizations are apprehensive about investing in multi-GPU setups. Additionally, data cleanup is a challenge that needs to be resolved, as data must be mature and pristine.
Takayuki Umehara - PeerSpot reviewer
Streamlined workflows with drag and drop convenience but needs enhancements in AI
I use Machine Learning Studio for system reselling and integration Machine Learning Studio is easy to use, with a significant feature being the drag and drop interface that enhances workflow without any complaints. It provides a return on investment and cost savings, proving beneficial for…

Quotes from Members

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

Pros

"There are numerous libraries available, and the documentation is rich and step-by-step, helping us understand which model to use in particular conditions."
"It is stable."
"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."
"I like that Hugging Face is versatile in the way it has been developed."
"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."
"The product is reliable."
"My preferred aspects are natural language processing and question-answering."
"The most valuable features are the inference APIs as it takes me a long time to run inferences on my local machine."
"The ability to do the templating and be able to transfer it so that I can easily do multiple types of models and data mining is a valuable aspect of this solution. You only have to set up the flows, the templates, and the data once and then you can make modifications and test different segmentations throughout."
"The solution is integrated with our Microsoft Azure tenant, and we don't have to go anywhere else outside the tenant."
"Overall, I rate Microsoft Azure Machine Learning Studio a seven out of ten."
"Their web interface is good."
"The most valuable feature of the solution is the availability of ChatGPT in the solution."
"It's easy to deploy."
"Machine Learning Studio is easy to use, with a significant feature being the drag and drop interface that enhances workflow without any complaints."
"The product's initial setup phase is easy."
 

Cons

"The area that needs improvement would be the organization of the materials. It could be clearer and more systematic. It would be good if the layout was clear and we could search the models easily."
"Regarding scalability, I'm finding the multi-GPU aspect of it challenging."
"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."
"Access to the models and datasets could be improved."
"Hugging Face could improve by implementing a search engine or chat bot feature similar to ChatGPT."
"I believe Hugging Face has some room for improvement. There are some security issues. They provide code, but API tokens aren't indicated. Also, the documentation for particular models could use more explanation. But I think these things are improving daily. The main change I'd like to see is making the deployment of inference endpoints more customizable for users."
"Initially, I faced issues with the solution's configuration."
"Overall, the icons in the solution could be improved to provide better guidance to users. Additionally, the setup process for the solution could be made easier."
"Performance is very poor."
"The data processor can pose a bit of a challenge, but the real complexity is determined by the skill of the implementation team."
"The solution should be more customizable. There should be more algorithms."
"A problem that I encountered was that I had to pay for the model that I wanted to deploy and use on Azure Machine Learning, but there wasn't any option that that model can be used in the designer."
"In future releases, I would like to see better integration with Power BI within Microsoft Azure Machine Learning Studio."
"Machine Learning Studio is more dependent on legacy Machine Learning algorithms. It would be beneficial for them to incorporate more services required for LLMs or LLM evaluation."
"The regulatory requirements of the product need improvement."
 

Pricing and Cost Advice

"So, it's requires expensive machines to open services or open LLM models."
"The solution is open source."
"We do not have to pay for the product."
"Hugging Face is an open-source solution."
"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."
"From a developer's perspective, I find the price of this solution high."
"The solution cost is high."
"I rate the product price as a nine on a scale of one to ten, where ten means it is very expensive."
"The solution operates on a pay-per-use model."
"The product is not that expensive."
"The platform's price is low."
"My team didn't deal with the licensing for Microsoft Azure Machine Learning Studio, so I'm unable to comment on pricing, but the money that was spent on the tool was worth it."
"There is a license required for this solution."
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Top Industries

By visitors reading reviews
Computer Software Company
11%
Financial Services Firm
11%
University
10%
Manufacturing Company
10%
Financial Services Firm
13%
Computer Software Company
11%
Manufacturing Company
10%
Healthcare Company
7%
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
 

Questions from the Community

What do you like most about Hugging Face?
My preferred aspects are natural language processing and question-answering.
What needs improvement with Hugging Face?
Access to the models and datasets could be improved. Many interesting ones are restricted. It would be great if they provided access for students or non-professionals who just want to test things.
What is your primary use case for Hugging Face?
This is a simple personal project, non-commercial. As a student, that's all I do.
Which do you prefer - Databricks or Azure Machine Learning Studio?
Databricks gives you the option of working with several different languages, such as SQL, R, Scala, Apache Spark, or Python. It offers many different cluster choices and excellent integration with ...
What do you like most about Microsoft Azure Machine Learning Studio?
The learning curve is very low. Operationalizing the model is also very easy within the Azure ecosystem.
What is your experience regarding pricing and costs for Microsoft Azure Machine Learning Studio?
Pricing is considered to be top-segment and should be improved. I rate the pricing as three or four on a scale of one to ten in terms of affordability.
 

Also Known As

No data available
Azure Machine Learning, MS Azure Machine Learning Studio
 

Overview

 

Sample Customers

Information Not Available
Walgreens Boots Alliance, Schneider Electric, BP
Find out what your peers are saying about Hugging Face vs. Microsoft Azure Machine Learning Studio and other solutions. Updated: March 2025.
844,944 professionals have used our research since 2012.