Azure OpenAI vs Hugging Face comparison

Microsoft and Hugging Face are both solutions in the AI Development Platforms category. Microsoft is ranked #1 with an average rating of 7.9, while Hugging Face is ranked #5 with an average rating of 8.2. Microsoft holds a 17.5% mindshare in AIDP, compared to Hugging Face’s 13.2% mindshare. Additionally, 93% of Microsoft users are willing to recommend the solution, compared to 100% of Hugging Face users who would recommend it.

Azure OpenAI

Read 32 Azure OpenAI reviews

8,535 Views
7,918 Comparison Views

93% willing to recommend

Hugging Face

Read 10 Hugging Face reviews

4,910 Views
4,171 Comparison Views

100% willing to recommend

Azure OpenAI

Hugging Face

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Executive SummaryUpdated on Jan 12, 2025

Azure OpenAI and Hugging Face are two major contenders in the AI platform space. Azure OpenAI, with its GPT-based models, has a slight advantage in conversational capabilities and enterprise integration, though it demands a higher investment.

Features: Azure OpenAI provides seamless integration with Microsoft's ecosystem, advanced query handling, and language understanding using GPT models, along with robust natural language processing and security features through Azure's infrastructure. Hugging Face offers open-source model access, diverse packages, and a modular approach highly suitable for prototyping, along with freely accessible documentation and trending models.

Room for Improvement: Azure OpenAI needs to tackle latency issues, enhance cost transparency, and improve support for regional languages along with scalability and integration enhancements. Hugging Face could improve its security support, documentation, and deployment customizability despite its advantages in open-source flexibility.

Ease of Deployment and Customer Service: Azure OpenAI supports a wide range of cloud environments, including hybrid setups, with support from Microsoft channels, although users report some inconsistencies. Hugging Face is ideal for on-premises setups; its open-source nature simplifies deployment but presents some support limitations due to the lack of dedicated support.

Pricing and ROI: Azure OpenAI comes with a higher cost, backed by strong enterprise features and scalability, appealing to firms investing heavily in Microsoft, with substantial productivity-driven ROI potential. In contrast, Hugging Face offers a more budget-friendly, low-entry barrier solution, promoting cost savings with its open-source offerings, making choice dependent on an organization's budget and specific needs.

To learn more, read our detailed Azure OpenAI vs. Hugging Face Report (Updated: January 2025).

Buyer's Guide

Azure OpenAI vs. Hugging Face

January 2025

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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

Ranking in other categories

No ranking in other categories

Hugging Face

Ranking in AI Development Platforms

5th

Average Rating

8.2

Reviews Sentiment

7.1

Number of Reviews

Ranking in other categories

No ranking in other categories

Mindshare comparison

As of January 2025, in the AI Development Platforms category, the mindshare of Azure OpenAI is 17.5%, down from 19.2% compared to the previous year. The mindshare of Hugging Face is 13.2%, up from 6.3% compared to the previous year. It is calculated based on PeerSpot user engagement data.

AI Development Platforms

Featured Reviews

Anup Karade

Associate Vice President at LTIMindtree

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.

Read full review

AshishKumar11

Associate Software Engineer at Linkfields Innovations (Pty) Ltd

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…

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Quotes from Members

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

Pros

"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."

"I would rate it a nine out of ten."

"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."

"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."

"Azure's integration with OpenAI's GPT is beneficial as well, especially for text generation tasks."

"GPT was useful for our projects."

"The high precision of information extraction is the most valuable feature."

"We can use the solution to implement our tasks and models quickly."

More Azure OpenAI pros

"My preferred aspects are natural language processing and question-answering."

"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."

"There are numerous libraries available, and the documentation is rich and step-by-step, helping us understand which model to use in particular conditions."

"The product is reliable."

"It is stable."

"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."

"Hugging Face provides open-source models, making it the best open-source and reliable solution."

"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."

More Hugging Face pros

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 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. The accuracy in these languages requires improvement."

"Azure OpenAI will be expensive if you want to implement it as a permanent solution for a customer."

"There could be an ability to generate visual data, such as architecture diagrams."

"There are no available updates of information that are currently provided."

"One major drawback of Azure OpenAI is its availability, as it's not consistently accessible for effective use."

"We encountered challenges related to question understanding."

"The solution needs to accommodate smaller companies."

More Azure OpenAI cons

"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."

"Most people upload their pre-trained models on Hugging Face, but more details should be added about the models."

"Implementing a cloud system to showcase historical data would be beneficial."

"Hugging Face could improve by implementing a search engine or chat bot feature similar to ChatGPT."

"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."

"The solution must provide an efficient LLM."

"It can incorporate AI into its services."

More Hugging Face cons

Pricing and Cost Advice

"I rate the product pricing six out of ten."

"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 cost structure depends on the volume of data processed and the computational resources required."

"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 platform offers a flexible pricing model which depends on the features and capabilities we utilize."

"The licensing is interaction-based, meaning transactional. It's reasonably priced for now."

"While the product meets our business requirements well, I consider it relatively expensive, especially for individual users like myself."

"The tool costs around 20 dollars a month."

More Azure OpenAI pricing and cost advice

"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."

"So, it's requires expensive machines to open services or open LLM models."

"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."

"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%

Educational Organization

Manufacturing Company

11%

Computer Software Company

11%

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.

See all answers

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.

See all answers

What needs improvement with Azure OpenAI?

Maybe with the next release, the response will be more precise and more human-like.

See all answers

What do you like most about Hugging Face?

My preferred aspects are natural language processing and question-answering.

See all answers

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...

See all answers

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...

See all answers

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Compared 49% of the time

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Compared 12% of the time

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Compared 6% of the time

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Compared 5% of the time

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Compared 5% of the time

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Product Reports

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Azure OpenAI

January 2025

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Buyer's Guide

AI Development Platforms

January 2025

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Microsoft

Hugging Face

Overview

Azure OpenAI integrates advanced language models with robust security for precise information extraction and task automation. Its seamless Azure integration and drag-and-drop interface simplify implementation and enhance accessibility.

Azure OpenAI offers a comprehensive suite of features designed for efficient data processing and task automation. It provides high precision in extracting information and strong conversational capabilities, crucial for developing chatbots and customer support systems. Its integration with Azure ensures seamless data handling and security, addressing key enterprise requirements. Users can employ its versatile GPT models for diverse applications such as predictive analytics, summarizing large documents, and competitive benchmarking. Despite its strengths, it faces challenges like latency, inadequate regional support, and limited integration of new technologies. Improvements in model fine-tuning and more flexible configuration are desired by users.

What features make Azure OpenAI a reliable choice?

High Precision: Ensures accurate data extraction suitable for critical applications.
Conversational Capabilities: Facilitates the creation of sophisticated chatbots.
Azure Integration: Seamlessly connects with Azure services for enhanced functionality.
Automation Support: Automates repetitive tasks, improving efficiency.
Security Features: Offers robust protection for sensitive data.
Drag-and-Drop Environment: Simplifies user interaction and deployment.

What benefits and ROI should users consider?

Natural Language Processing: Provides superior language model performance.
Quick Implementation: Reduces time to deploy applications.
Scalability: Supports growing business needs with ease.
Versatile GPT Models: Applicable across different industry workflows.

Azure OpenAI is implemented across industries like healthcare, finance, and education for tasks like invoice processing, digitalizing records, and language translation. It enhances policy management, document assimilation, and customer support with predictive analytics and keyword extraction. Organizations in such sectors benefit from streamlined workflows and task automation.

Hugging Face offers a platform hosting a wide range of models with efficient natural language processing tools. Known for its open-source nature, comprehensive documentation, and a variety of embedding models, it reduces costs and facilitates easy adoption.

Valued in the tech community for its ability to host diverse models, Hugging Face simplifies tasks in machine learning and artificial intelligence. Users find it easy to fine-tune large language models like LLaMA for custom data training, access a library of open-source models for tailored applications, and utilize options like the Inference API. The platform impresses with its free usage, popularity of trending models, and effective program management, although improvements could be made in security and documentation for more customizable deployments. Collaboration with ecosystem library providers and better model description details could boost its utility.

What are the key features of Hugging Face?

Wide model hosting: Offers a vast array of models for diverse applications.
Inference API: Provides efficient and seamless deployment of models.
Open-source access: Encourages community collaboration and model sharing.
Embedding models: Supports various formats and packages.
Simple platform: User-friendly and cost-effective for businesses.

What benefits can users expect from Hugging Face?

Cost reduction: Access to free or lower-cost tools compared to proprietary software.
Customization: Easy adaptation of pre-trained models to fit specific needs.
Efficiency: Quick deployment and fine-tuning of models for business use.
Community support: Comprehensive documentation and open-source collaboration.

Hugging Face is widely used across industries requiring machine learning solutions, such as creating SQL chatbots or data extraction tools. Organizations focus on fine-tuning language models to enhance business processes and remove reliance on proprietary systems. The platform supports innovative applications, including business-specific AI solutions, demonstrating its flexibility and adaptability.

Buyer's Guide

Azure OpenAI vs. Hugging Face

January 2025

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