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Azure OpenAI vs Hugging Face comparison

 

Comparison Buyer's Guide

Executive SummaryUpdated on Apr 20, 2025

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

Azure OpenAI
Ranking in AI Development Platforms
1st
Average Rating
7.8
Reviews Sentiment
6.7
Number of Reviews
33
Ranking in other categories
No ranking in other categories
Hugging Face
Ranking in AI Development Platforms
4th
Average Rating
8.2
Reviews Sentiment
7.1
Number of Reviews
13
Ranking in other categories
No ranking in other categories
 

Mindshare comparison

As of April 2025, in the AI Development Platforms category, the mindshare of Azure OpenAI is 12.9%, down from 21.1% compared to the previous year. The mindshare of Hugging Face is 13.5%, up from 7.6% compared to the previous year. It is calculated based on PeerSpot user engagement data.
AI Development Platforms
 

Featured Reviews

Viswanath Barenkala - PeerSpot reviewer
Offers tools to moderate generated content and guidance to safely design applications, but it is not consistently accessible
Instead of a feature, the GPT-4 model has been most beneficial for automating tasks. We transitioned from GPT-3.5 to GPT-4 and actively use it. However, we face limitations due to geographic availability, subscription constraints, and rate limiting, which we are currently negotiating and working towards optimizing. While we haven't formally benchmarked Azure OpenAI's language understanding against industry standards, we find it performs well about 70-80% of the time. Occasionally, we need to refine our queries and adapt our systems accordingly to improve accuracy and effectiveness.
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.

Quotes from Members

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

Pros

"Our clients are interested in building knowledge bases, particularly in child welfare. In this domain, we focus on supporting caseworkers by compiling and organizing relevant information. This information is then stored in a database using a query. The database generates summaries and reminders for specific actions and even facilitates sending emails to parents or other relevant parties. The system's complexity is tailored to the specific needs of child welfare cases. Additionally, we're exploring opportunities to assist a healthcare organization. Specifically, we're working on streamlining the process of filling out forms required for insurance claims. This effort aims to ensure that hospitals can receive funding or payment for the care they provide."
"It's very powerful. It allows users to query our documents using natural language and receive answers in the same way. This makes our product information much more accessible than traditional keyword-based search."
"Azure OpenAI has significantly reduced costs and increased efficiency in tasks such as aggressive testing of systems to avoid anomalies and trust issues."
"Two aspects I appreciate are the turnaround time and ease of use. As it's a managed service, the quick turnaround is beneficial, and the simple interface makes it easy to work with. Performance and scalability are also strong points since you can scale as needed."
"Generative AI or GenAI seems to be the best part of the solution."
"We can use the solution to implement our tasks and models quickly."
"GPT was useful for our projects."
"OpenAI's models are more mature than Watson's. They offer a wider range of features and provide richer outputs."
"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."
"I would rate this product nine out of ten."
"There are numerous libraries available, and the documentation is rich and step-by-step, helping us understand which model to use in particular conditions."
"I appreciate the versatility and the fact that it has generalized many models."
"My preferred aspects are natural language processing and question-answering."
"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."
"I like that Hugging Face is versatile in the way it has been developed."
 

Cons

"I have found the tool unreliable in certain use cases. I aim to enhance the system's latency, particularly in responding to calls. Occasionally, calls don't respond, so I want to improve reliability."
"Azure OpenAI is not available in all regions, and its technical support should be improved."
"There is room for improvement in their support services."
"Azure OpenAI is not an optimized tool yet, making it one of its shortcomings where improvements are required."
"Azure could significantly benefit from including more LLM models apart from OpenAI, as I often need to switch clouds when a model doesn't meet my requirements."
"One major drawback of Azure OpenAI is its availability, as it's not consistently accessible for effective use."
"Sometimes, it gives answers in English, even when the request is in Polish."
"The product features themselves are fine. However, with Microsoft scaling the service so much, the support structure needs to keep pace. When solving complex issues, the process of interacting with Microsoft can be quite time-consuming."
"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."
"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."
"Most people upload their pre-trained models on Hugging Face, but more details should be added about the models."
"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."
"Implementing a cloud system to showcase historical data would be beneficial."
"Regarding scalability, I'm finding the multi-GPU aspect of it challenging."
"Hugging Face could improve by implementing a search engine or chat bot feature similar to ChatGPT."
"Initially, I faced issues with the solution's configuration."
 

Pricing and Cost Advice

"The platform offers a flexible pricing model which depends on the features and capabilities we utilize."
"We started with monthly payments, but we plan to switch to yearly billing once we've stabilized our solution."
"While the product meets our business requirements well, I consider it relatively expensive, especially for individual users like myself."
"The cost is quite high and fixed."
"I rate the product pricing six out of ten."
"Cost-wise, the product's price is a bit on the higher side."
"The tool costs around 20 dollars a month."
"According to the negotiations taking place and the contract, there is a need to make either monthly or yearly payments to use the solution."
"The solution is open source."
"So, it's requires expensive machines to open services or open LLM models."
"I recall seeing a fee of nine dollars, and there's also an enterprise option priced at twenty dollars per month."
"We do not have to pay for the product."
"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."
"Hugging Face is an open-source solution."
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Top Industries

By visitors reading reviews
Financial Services Firm
15%
Computer Software Company
13%
Manufacturing Company
11%
Educational Organization
6%
Computer Software Company
11%
Financial Services Firm
11%
University
10%
Manufacturing Company
10%
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
 

Questions from the Community

What do you like most about Azure OpenAI?
The product is easy to integrate with our IT workflow.
What is your experience regarding pricing and costs for Azure OpenAI?
In the past, the primary expense involved token limitations which constrained scaling. Recent iterations have increased token allowances, mitigating some challenges associated with concurrent user ...
What needs improvement with Azure OpenAI?
Azure ( /products/microsoft-azure-reviews ) could significantly benefit from including more LLM models apart from OpenAI, as I often need to switch clouds when a model doesn't meet my requirements....
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.
 

Overview

Find out what your peers are saying about Azure OpenAI vs. Hugging Face and other solutions. Updated: April 2025.
849,190 professionals have used our research since 2012.