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Azure OpenAI vs Hugging Face comparison

 

Comparison Buyer's Guide

Executive SummaryUpdated on Jan 12, 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
8.0
Reviews Sentiment
6.7
Number of Reviews
32
Ranking in other categories
No ranking in other categories
Hugging Face
Ranking in AI Development Platforms
5th
Average Rating
8.2
Reviews Sentiment
7.0
Number of Reviews
12
Ranking in other categories
No ranking in other categories
 

Mindshare comparison

As of February 2025, in the AI Development Platforms category, the mindshare of Azure OpenAI is 15.0%, down from 20.0% compared to the previous year. The mindshare of Hugging Face is 13.2%, up from 6.6% compared to the previous year. It is calculated based on PeerSpot user engagement data.
AI Development Platforms
 

Featured Reviews

Anup Karade - PeerSpot reviewer
Created a chatbot powered by OpenAI to answer HR, travel, and expense-related questions
We have created our own platform called CanvasAI. It provides machine-learning plumbing and integration with your services. So, we've integrated Azure OpenAI through Canvas. We're also looking at some hard interface modeling from AWS as well. We access Azure OpenAI solutions for our business workflow via the platform, not directly from the service provider. There are security considerations we're working through with the security team, etc. Canvas provides the control plane, which handles RBAC for user registration and model management. For internal processes, we're using OpenAI to create partner call reports, like summarizing quarterly zip code data and other financial models. We also use it for legal and contract stuff, like comparing SOWs and contracts. For user experience, we've created a chatbot powered by OpenAI to answer HR, travel, and expense-related questions.
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

"The product saves a lot of time."
"OpenAI integrates seamlessly with the broader Microsoft Azure ecosystem, and that provides synergies with the other solutions. This integration makes it much easier to build solutions."
"The AI search functionality is particularly effective, as it creates summaries from data."
"We can use the solution to implement our tasks and models quickly."
"The high precision of information extraction is the most valuable feature."
"The most valuable feature of Azure OpenAI stems from the GPT-3.5 models it provides to its users."
"Its versatility makes it incredibly useful for technical problem-solving, content creation, data analytics, and more."
"Generative AI or GenAI seems to be the best part of the solution."
"Hugging Face provides open-source models, making it the best open-source and reliable solution."
"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."
"My preferred aspects are natural language processing and question-answering."
"The solution is easy to use compared to other frameworks like PyTorch and TensorFlow."
"I like that Hugging Face is versatile in the way it has been developed."
"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 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."
 

Cons

"In the next release, they could enhance the product's features for even greater usability and efficiency."
"There are no available updates of information that are currently provided."
"Azure needs to work on its own model development and improve the integration of voice-to-text services, particularly for right-to-left languages such as Arabic and Urdu."
"We encountered challenges related to question understanding."
"I noticed there are no instructional videos or guides on the network portal for initial configurations. There is limited information available, and this is a concern for me. I would like to see more resources and guides to address these issues."
"There could be an ability to generate visual data, such as architecture diagrams."
"Since we don't train the model on our data, it's a struggle to ensure OpenAI answers questions exclusively from our data. During user testing, we found ways to make the system provide answers from outside sources."
"The fine-tuning of models with the use of Azure OpenAI is an area with certain shortcomings currently, and it can be considered for improvement in the future."
"Most people upload their pre-trained models on Hugging Face, but more details should be added about the models."
"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."
"Access to the models and datasets could be improved."
"Regarding scalability, I'm finding the multi-GPU aspect of it challenging."
"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."
"Initially, I faced issues with the solution's configuration."
"Hugging Face could improve by implementing a search engine or chat bot feature similar to ChatGPT."
 

Pricing and Cost Advice

"Cost-wise, the product's price is a bit on the higher side."
"It's a token-based system, so you pay per token used by the model."
"I'm uncertain about the licensing, specifically the pricing. This falls under the purview of other teams, particularly the sales teams. I am not informed about the pricing details."
"Azure OpenAI is a bit more expensive than other services."
"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 pricing is acceptable, and it's delivering good value for the results and outcomes we need."
"The licensing is interaction-based, meaning transactional. It's reasonably priced for now."
"The tool costs around 20 dollars a month."
"I recall seeing a fee of nine dollars, and there's also an enterprise option priced at twenty dollars per month."
"Hugging Face is an open-source solution."
"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."
"So, it's requires expensive machines to open services or open LLM models."
"The solution is open source."
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Top Industries

By visitors reading reviews
Financial Services Firm
14%
Computer Software Company
13%
Manufacturing Company
11%
Government
6%
Computer Software Company
10%
Manufacturing Company
10%
Financial Services Firm
10%
University
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?
The pricing is very good for handling various kinds of jobs. While small jobs are manageable, more complex jobs require a higher model, which is a bit challenging.
What needs improvement with Azure OpenAI?
Maybe with the next release, the response will be more precise and more human-like.
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: January 2025.
838,640 professionals have used our research since 2012.