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 13.9% mindshare in AIDP, compared to Hugging Face’s 13.4% 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

8,832 Views
8,115 Comparison Views

93% willing to recommend

Hugging Face

Read 12 Hugging Face reviews

5,656 Views
4,555 Comparison Views

100% willing to recommend

Azure OpenAI

Hugging Face

Comparison Buyer's Guide

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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: March 2025).

Buyer's Guide

Azure OpenAI vs. Hugging Face

March 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

7.0

Number of Reviews

Ranking in other categories

No ranking in other categories

Mindshare comparison

As of March 2025, in the AI Development Platforms category, the mindshare of Azure OpenAI is 13.9%, down from 20.9% compared to the previous year. The mindshare of Hugging Face is 13.4%, up from 7.0% compared to the previous year. It is calculated based on PeerSpot user engagement data.

AI Development Platforms

Featured Reviews

Viswanath Barenkala

Associate Vice President at State Street

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.

Read full review

SwaminathanSubramanian

Director/Enterprise Solutions Architect, Technology Advisor at Kyndryl

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.

Read full review

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."

"Azure OpenAI is useful for benchmarking products."

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

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

"Azure OpenAI has significantly reduced costs and increased efficiency in tasks such as aggressive testing of systems to avoid anomalies and trust issues."

"The AI search functionality is particularly effective, as it creates summaries from data."

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

"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."

More Azure OpenAI pros

"It is stable."

"I like that Hugging Face is versatile in the way it has been developed."

"I appreciate the versatility and the fact that it has generalized many models."

"The solution is easy to use compared to other frameworks like PyTorch and TensorFlow."

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

"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."

More Hugging Face pros

Cons

"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."

"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."

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

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

"The UI could be a little easier."

"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."

"Sometimes, it gives answers in English, even when the request is in Polish."

"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."

More Azure OpenAI cons

"Access to the models and datasets could be improved."

"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 solution must provide an efficient LLM."

"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."

"Access to the models and datasets could be improved. Many interesting ones are restricted."

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

"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."

More Hugging Face cons

Pricing and Cost Advice

"The cost is quite high and fixed."

"The solution's pricing is normal worldwide but expensive in Turkey because Turkey's currency is different."

"If you consider the long-term aspect of any project, Azure OpenAI is a costly solution."

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

"The cost is pretty high. Even by US standards, you would find it high."

"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."

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

"The pricing is acceptable, and it's delivering good value for the results and outcomes we need."

More Azure OpenAI pricing and cost advice

"We do not have to pay for the product."

"The solution is open source."

"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."

"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."

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Top Industries

By visitors reading reviews

Financial Services Firm

15%

Computer Software Company

13%

Manufacturing Company

11%

Educational Organization

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.

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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.

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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.

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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.

See all answers

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.

See all answers

Comparisons

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Compared 44% of the time

Amazon SageMaker vs Azure OpenAI

Compared 12% of the time

Microsoft Azure Machine Learning Studio vs Azure OpenAI

Compared 9% of the time

Google Cloud AI Platform vs Azure OpenAI

Compared 8% of the time

IBM Watson Studio vs Azure OpenAI

Compared 3% of the time

More Azure OpenAI Competitors

Replicate vs Hugging Face

Compared 43% of the time

Google Vertex AI vs Hugging Face

Compared 11% of the time

GroqCloud Platform vs Hugging Face

Compared 9% of the time

Together Inference vs Hugging Face

Compared 5% of the time

Fireworks AI vs Hugging Face

Compared 4% of the time

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Product Reports

Buyer's Guide

Azure OpenAI

March 2025

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Buyer's Guide

AI Development Platforms

February 2025

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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.

Microsoft

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.

Hugging Face

Buyer's Guide

Azure OpenAI vs. Hugging Face

March 2025

Free Report: Azure OpenAI vs. Hugging Face

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