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Azure OpenAI vs Hugging Face comparison

 

Comparison Buyer's Guide

Executive SummaryUpdated on Oct 8, 2024
 

Categories and Ranking

Azure OpenAI
Ranking in AI Development Platforms
1st
Average Rating
8.0
Reviews Sentiment
8.4
Number of Reviews
30
Ranking in other categories
No ranking in other categories
Hugging Face
Ranking in AI Development Platforms
5th
Average Rating
8.2
Number of Reviews
10
Ranking in other categories
No ranking in other categories
 

Mindshare comparison

As of November 2024, in the AI Development Platforms category, the mindshare of Azure OpenAI is 22.2%, up from 17.3% compared to the previous year. The mindshare of Hugging Face is 7.8%, up from 5.6% 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.
AshishKumar11 - PeerSpot reviewer
Open-sourced, reliable, and enables organizations to finetune data for business requirements
Hugging Face is a website that provides various open-source models. We use them to finetune models for our business. It is just like ChatGPT, but ChatGPT has paid sources. If we have to call an API, we must pay for it. However, Hugging Face has various open-source models like Llama 2 and Llama 3…

Quotes from Members

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

Pros

"Its versatility makes it incredibly useful for technical problem-solving, content creation, data analytics, and more."
"The solution has a very drag-and-drop environment. Instead of coding something from scratch or understanding any concept in extensive depth before deployment, this is good. Plus, they have an auto dataset, which means you can choose any dataset they have instead of providing your own. So that's also pretty nice."
"Azure OpenAI has significantly reduced costs and increased efficiency in tasks such as aggressive testing of systems to avoid anomalies and trust issues."
"My goal was to create an experience where project managers don't have to read through entire documents. Instead, they can ask a question and receive relevant point analysis. This analysis identifies the document and specific section where the information resides. Previously, users had to rely on these document references. Now, Azure OpenAI enhances the experience by providing the answer directly in the user's own language, relevant to their context."
"The most crucial aspect is the conversational capability, where you can simply ask questions, and it provides answers based on your content and documents, particularly tailored to your specific environment."
"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."
"Azure OpenAI is very easy to use instead of AWS services."
"The solution is easy to use compared to other frameworks like PyTorch and TensorFlow."
"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."
"Hugging Face provides open-source models, making it the best open-source and reliable solution."
"It is stable."
"There are numerous libraries available, and the documentation is rich and step-by-step, helping us understand which model to use in particular conditions."
"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

"I faced one issue with Azure OpenAI: My customer wanted more clarity on the pricing. They were not able to get proper answers from the documentation or the pricing calculator. I suggest that Microsoft maintain standardization in the pricing details published in the documentation and the pricing calculator."
"There are certain shortcomings with the product's scalability and support team where improvements are required."
"Sometimes, it gives answers in English, even when the request is in Polish."
"Azure OpenAI will be expensive if you want to implement it as a permanent solution for a customer."
"One area for improvement is providing more flexibility in configuration and connectivity with external tools."
"We are awaiting the new updates like multi-model capabilities."
"The UI could be a little easier."
"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."
"Hugging Face could improve by implementing a search engine or chat bot feature similar to ChatGPT."
"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."
"Initially, I faced issues with the solution's configuration."
"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."
"It can incorporate AI into its services."
"The solution must provide an efficient LLM."
 

Pricing and Cost Advice

"The cost structure depends on the volume of data processed and the computational resources required."
"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."
"The solution's pricing is normal worldwide but expensive in Turkey because Turkey's currency is different."
"The solution's pricing depends on the services you will deploy."
"Cost-wise, the product's price is a bit on the higher side."
"While the product meets our business requirements well, I consider it relatively expensive, especially for individual users like myself."
"If you consider the long-term aspect of any project, Azure OpenAI is a costly solution."
"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 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."
"I recall seeing a fee of nine dollars, and there's also an enterprise option priced at twenty dollars per month."
"The solution is open source."
"We do not have to pay for the product."
"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%
Manufacturing Company
10%
University
10%
Financial Services Firm
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 really depends on the specific requirements and underlying needs. For example, if the goal is to implement innovative solutions for the future or to improve productivity in decision-mak...
What needs improvement with Azure OpenAI?
For my needs, when working with interactive dashboards, it's expensive. I would prefer a system that provides alternative dashboard options or allows me to go directly into the program and pinpoint...
What do you like most about Hugging Face?
My preferred aspects are natural language processing and question-answering.
What needs improvement with Hugging Face?
Hugging Face could improve by implementing a search engine or chat bot feature similar to ChatGPT. This would aid developers in easily finding how to fine-tune models with specific data or get mode...
What is your primary use case for Hugging Face?
I use Hugging Face primarily to work with open LLM models. I recently started using the open LOM models and also use embedding models. I use these models to train custom data and monitor our deskto...
 

Learn More

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Overview

Find out what your peers are saying about Azure OpenAI vs. Hugging Face and other solutions. Updated: October 2024.
816,406 professionals have used our research since 2012.