Hugging Face Reviews

Name: Hugging Face
Brand: Hugging Face
Rating: 4.1 (13 reviews)

4.1 out of 5

13 reviews
100% willing to recommend

What is Hugging Face?

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.

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Hugging Face is the #3 ranked solution in top AI Development Platforms. PeerSpot users give Hugging Face an average rating of 8.2 out of 10. Hugging Face is most commonly compared to Google Vertex AI: Hugging Face vs Google Vertex AI. Hugging Face is popular among the large enterprise segment, accounting for 58% of users researching this solution on PeerSpot. The top industry researching this solution are professionals from a computer software company, accounting for 11% of all views.

Helped 856,873 peers since 2012

Featured Hugging Face reviews

SwaminathanSubramanian

Director/Enterprise Solutions Architect, Technology Advisor at Kyndryl

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.

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Melek Ghouma

Student at Renater

I've been trying to implement some chatbots, and having free access to Hugging Face helped me a lot. I use PyTorch and TensorFlow to implement other deep-learning models and access LLMs. Each one of these tools has its own purpose. Python is used for deep learning projects to train and fine-tune models at the deep learning level, while for Hugging Face, it's mainly for the transformers library and LLM APIs. I cannot compare them directly. For me, it's about access to datasets and models. I would rate this product nine out of ten.

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Neeraj Maurya

Artificial Intelligence Consultant at GlobalLogic

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 desktop custom models after training and deployment The most valuable features of Hugging Face are the…

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Hugging Face mindshare

As of June 2025, the mindshare of Hugging Face in the AI Development Platforms category stands at 13.1%, up from 8.6% compared to the previous year, according to calculations based on PeerSpot user engagement data.

AI Development Platforms

PeerResearch reports based on Hugging Face reviews

Type	Title	Date
Category	AI Development Platforms	Jun 15, 2025	Download
Product	Reviews, tips, and advice from real users	Jun 15, 2025	Download
Comparison	Hugging Face vs Azure OpenAI	Jun 15, 2025	Download
Comparison	Hugging Face vs Google Vertex AI	Jun 15, 2025	Download
Comparison	Hugging Face vs Microsoft Azure Machine Learning Studio	Jun 15, 2025	Download

Title	Rating	Mindshare	Recommending
Google Vertex AI	4.2	12.5%	100%	10 interviews Add to research
Microsoft Azure Machine Learning Studio	3.9	6.5%	93%	62 interviews Add to research

Valuable Features

Hugging Face's most valuable aspects include access to numerous models, open-source libraries, comprehensive documentation, and versatile functionalities for natural language processing and question-answering. Users appreciate the platform's easy-to-use interface, trending model showcase, and rich datasets. The Inference API aids in seamless model deployment. It assists organizations in reducing costs with its free and reliable offerings. Hugging Face serves as a central hub for AI model exploration and implementation.

"Overall, the platform is excellent."
"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."

Room for Improvement

Hugging Face requires a more systematic organization of materials with improved search features. Historical data via a cloud system would help. Security issues, unclear documentation, and limited access to models and datasets are areas for enhancement. There are delays in updating ecosystem libraries, and more model details should be available. Accessibility and deployment of inference endpoints need customization. Multi-GPU training and data cleanup present challenges. Access for non-professional users and improvements in AI integration are suggested.

"The initial setup can be rated as a seven out of ten due to occasional issues during model deployment, which might require adjustments."
"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."
"Regarding scalability, I'm finding the multi-GPU aspect of it challenging."

Pricing

Hugging Face offers different pricing models, with enterprise options available. Some pricing details mention $6 to $9 monthly fees, with an enterprise option at $20 per month. Hugging Face is primarily open-source, allowing free model downloads to local machines. However, costs can increase with extensive machine and GPU requirements for product services and large language models. Additional deployment costs may arise with cloud services, depending on specific usage.

"Hugging Face is an open-source solution."
"We do not have to pay for the product."
"The solution is open source."

Popular Use Cases

Users employ Hugging Face primarily for machine learning and AI tasks, focusing on large language models like LLaMA. They utilize it to fine-tune models for specific projects, such as converting English requests into SQL queries or extracting data from PDFs. Users download open-source models to train on custom data, tailoring them to fields like medicine and law. They appreciate that Hugging Face offers open-source models, enabling customization without the costs associated with proprietary solutions.

Service and Support

Users express satisfaction with Hugging Face's customer service, particularly highlighting positive experiences with support tickets for technical queries. Some prefer contacting through email, although several have not interacted with support due to comprehensive documentation. This extensive resource availability reduces the need for direct contact with customer support, indicating effective informational support and user guidance.

Deployment

Users find Hugging Face's initial setup varies in difficulty. It's considered easy, often taking only minutes, but challenges arise depending on the infrastructure, like Kubernetes or Docker. For cloud deployment, issues are minimal; on-premises require monitoring. Installation depends on hardware quality. Education or tutorials aid deployment, particularly for data science students. Recent updates have eased the process, and many rate the setup favorably, though model suitability for customized cases can be time-consuming to assess.

Scalability

Hugging Face is widely regarded as scalable, with many entities extolling its versatility for scaling up knowledge and model upgrades. Although some organizations express concerns about production deployment due to the necessity for managed offerings, it remains adaptable for model enhancement. While not yet suitable for mature production environments, multiple users appreciate its flexibility for prototype-level applications, highlighting the potential for handling numerous users efficiently.

Stability

Hugging Face is generally considered stable, with most users rating it eight or nine out of ten for reliability. Many users report no major bugs or crashes. Some experience rate-limiting and server errors, noting stability is enhanced by controlled environments and tailored models. Users appreciate its role in deploying large language models despite some development work needed. Feedback highlights Hugging Face's stability as beneficial for AI development.

These insights are based on the in-depth reviews provided by peers to help you make a better buying decision.

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Review data by company size

By reviewers

By visitors reading reviews

Top industries

By visitors reading reviews

Computer Software Company

11%

Manufacturing Company

10%

Financial Services Firm

10%

University

Comms Service Provider

Educational Organization

Healthcare Company

Government

Retailer

Non Profit

Construction Company

Media Company

Hospitality Company

Outsourcing Company

Real Estate/Law Firm

Energy/Utilities Company

Legal Firm

Insurance Company

Performing Arts

Consumer Goods Company

International Affairs Institute

Pharma/Biotech Company

Logistics Company

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Learn more about Hugging Face

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.