Hugging Face Reviews

Name: Hugging Face
Brand: Hugging Face
Rating: 4 (4 reviews)

4.4 out of 5

4 reviews
100% willing to recommend

What is Hugging Face?

The AI community building the future. Build, train and deploy state of the art models powered by the reference open source in machine learning.

Get the AI Development Platforms Buyer's Guide and find out what your peers are saying about Hugging Face, Microsoft Azure Machine Learning Studio, Amazon SageMaker and more!

Hugging Face is the #6 ranked solution in top AI Development Platforms. PeerSpot users give Hugging Face an average rating of 8.8 out of 10. Hugging Face is most commonly compared to Microsoft Azure Machine Learning Studio: Hugging Face vs Microsoft Azure Machine Learning Studio. Hugging Face is popular among the large enterprise segment, accounting for 61% of users researching this solution on PeerSpot. The top industry researching this solution are professionals from a computer software company, accounting for 12% of all views.

Buyer's Guide

AI Development Platforms

June 2024

Get the category report

Helped 793,295 peers since 2012

Featured reviews

Vikas Singhvi

Founder at AIM

I started using Hugging Face because I'm still prototyping. Other vendors are pretty managed offerings with many costs in getting code built out, whereas Hugging Face is free. Alternatives like Vertex OpenAI and Azure OpenAI offer access to large language models, but most platforms are closed and restrict fine-tuning. This is where Hugging Face shines. Its open nature allows for fine-tuning of models, providing a significant advantage. Additionally, if data security is a concern, enterprises can deploy their own Hugging Face model as an endpoint or local instance, avoiding the need to send data to externally managed offerings. This flexibility and control over data makes Hugging Face a compelling choice for producing large language models.

Read full review

Seza Dursun

Senior Data Science Consultant at TUPRAS

My preferred aspects are natural language processing and question-answering. It aids us in efficiently discovering effective features and models. The ability to enlarge and tag faces has assisted me in finding effective and well-documented packages. I incorporate their favored methods and utilize…

Read full review

Neeraj Pokala

Machine Learning Engineer at TechMinfy

We have two deployment options: cloud and on-premises. On-premises means it's on-demand, and we have to monitor it. With cloud deployment, there's no need to watch for availability because it's always handled in the cloud. There should be no problems with cloud deployment. If we deploy on-premises, we have to monitor it ourselves. That's the main difference. We have both options available. It's very easy to deploy an endpoint because there's already pre-built documentation. With just one click, you can directly load the knowledge handler. The challenging part is determining if the model suits our customized use case, which takes time. Once we're sure the model is right for our use case, it's straightforward.

Read full review

Hugging Face mindshare

As of July 2024, the mindshare of Hugging Face in the AI Development Platforms category stands at 8.8%, up from 6.9% compared to the previous year, according to calculations based on PeerSpot user engagement data.

AI Development Platforms

Key learnings from peers

Last updated Jul 26, 2024

Valuable Features

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

Room for Improvement

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

Pricing

"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."
"I recall seeing a fee of nine dollars, and there's also an enterprise option priced at twenty dollars per month."

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

Download our AI Development Platforms Buyer's Guide for additional reliable information.