Try our new research platform with insights from 80,000+ expert users

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

"I appreciate the versatility and the fact that it has generalized many models."
"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."
"The most valuable features are the inference APIs as it takes me a long time to run inferences on my local machine."
"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."
"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."
"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."
"The platform as a service provides user-friendly instruments, making the experience easy."
"Anyone who isn't a programmer his whole life can adopt it. All he needs is statistics and data analysis skills."
"It's good for citizen data scientists, but also, other people can use Python or .NET code."
"Auto email and studio are great features."
"Microsoft Azure Machine Learning Studio offers a hands-on experience with lots of flexibility."
"MLS allows me to set up data experiments by running through various regression and other machine learning algorithms, with different data cleaning and treatment tools. All of this can be achieved via drag and drop, and a few clicks of the mouse."
"It helps in building customized models, which are easy for clients to use​.​​"
"The initial setup is very simple and straightforward."
 

Cons

"Access to the models and datasets could be improved. Many interesting ones are restricted."
"It can incorporate AI into its services."
"Initially, I faced issues with the solution's configuration."
"The solution must provide an efficient LLM."
"Access to the models and datasets could be improved."
"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."
"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."
"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."
"The solution's initial setup process is complicated."
"The pricing policy should be improved. I find the pricing to be not a good story in this case, as it is not affordable for everyone."
"In terms of improvement, I'd like to have more ability to construct and understand the detailed impact of the variables on the model. Their algorithms are very powerful and they explain overall the net contribution of each of the variables to the solution. In terms of being able to say to people "If you did this, you'll get this much more improvement" it wasn't great."
"Using the solution requires some specific learning which can take some time."
"There's room for improvement in terms of binding the integration with Azure DevOps."
"Performance is very poor."
"One area where Azure Machine Learning Studio could improve is its user interface structure."
"In the Machine Learning Studio, particularly the Designer part, which is essentially Azure's demo designer, there is room for improvement. Many customers and users tend to switch to Microsoft Azure Multi-Joiners, which is a more basic version, but they do so internally. One area that could use enhancement is the process of connecting components. Currently, every time you want to connect a component, such as linking it to your storage or an instance like EC2, you have to input your username and password repeatedly. This can be quite cumbersome. Google, for instance, has made it more user-friendly by allowing easy access for connecting services within a workspace. In a workspace, you can set up various resources like storage, a database cluster, machine learning studio, and more. When connecting these services, there's no need to enter your username and password each time, making it a more efficient process. Another aspect to consider is the role of the designer, and they were to integrate a large language model to handle various tasks, it could significantly enhance the overall scalability and usability of the platform."
 

Pricing and Cost Advice

"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."
"There isn’t any such expensive costs and only a standard license is required."
"It is less expensive than one of its competitors."
"There is a lack of certainty with the solution's pricing."
"The solution cost is high."
"On a scale from one to ten, with ten being overpriced, I would rate the price of this solution at six."
"I used the free student license for a few months to operate the solution, but I'll have to pay for it if I want to do more now."
"I would rate the pricing an eight out of ten, with ten being very expensive. Not very expensive, not very cheap."
"I am paying for it following a pay-as-you-go. So, the more I use it, the more it costs."
report
Use our free recommendation engine to learn which AI Development Platforms solutions are best for your needs.
845,040 professionals have used our research since 2012.
 

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.
845,040 professionals have used our research since 2012.