Hugging Face vs TensorFlow comparison

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Hugging Face

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Executive Summary

We performed a comparison between Hugging Face and TensorFlow based on real PeerSpot user reviews.

Find out in this report how the two AI Development Platforms solutions compare in terms of features, pricing, service and support, easy of deployment, and ROI.

To learn more, read our detailed Hugging Face vs. TensorFlow Report (Updated: October 2024).

Hugging Face vs. TensorFlow

October 2024

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Categories and Ranking

Hugging Face

Ranking in AI Development Platforms

5th

Average Rating

8.2

Number of Reviews

Ranking in other categories

No ranking in other categories

TensorFlow

Ranking in AI Development Platforms

6th

Average Rating

8.8

Number of Reviews

Ranking in other categories

No ranking in other categories

Mindshare comparison

As of November 2024, in the AI Development Platforms category, the mindshare of Hugging Face is 7.8%, up from 5.6% compared to the previous year. The mindshare of TensorFlow is 5.7%, down from 9.8% compared to the previous year. It is calculated based on PeerSpot user engagement data.

AI Development Platforms

Featured Reviews

AshishKumar11

Associate Software Engineer at Linkfields Innovations (Pty) Ltd

Jul 25, 2024

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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Ashish Upadhyay

Founder at BlockMosiac

Nov 6, 2023

A robust tools for model visualization and debugging with superior scalability and stability, and an intuitive user-friendly interface

The one feature we find most valuable at our company is its robust and flexible machine-learning capabilities. It empowers us to seamlessly create and deploy machine learning models, offering a versatile solution for implementing sophisticated environments and various types of AI solutions. The ability to develop and fine-tune models, such as risk assessment for detection and market protection, as well as the creation of recommendation systems, is paramount. This versatility extends to providing personalized identity-relevant applications for our enterprise clients, delivering valuable insights to the market. Its exceptional support for deep learning and its efficient resource utilization enable us to undertake complex financial and data analyses. The flexibility it provides is crucial for meeting industrial requirements and crafting solutions tailored to our client's specific needs.

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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

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

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

"The product is reliable."

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

"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."

"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."

"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."

More Hugging Face pros

"Our clients were not aware they were using TensorFlow, so that aspect was transparent. I think we personally chose TensorFlow because it provided us with more of the end-to-end package that you can use for all the steps regarding billing and our models. So basically data processing, training the model, evaluating the model, updating the model, deploying the model and all of these steps without having to change to a new environment."

"The most valuable feature of TensorFlow is deep learning. It is the best tool for deep learning in the market."

"It provides us with 35 features like patch normalization layers, and it is easy to implement using the Kras library when the Kaspersky flow is running behind it."

"Google is behind TensorFlow, and they provide excellent documentation. It's very thorough and very helpful."

"TensorFlow improves my organization because our clients get a lot of investment from their investors and we are progressively improving the products. Every six months we release new features."

"Edge computing has some limited resources but TensorFlow has been improving in its features. It is a great tool for developers."

"I would rate the solution an eight out of ten. I am not a developer but more of an account manager. I can find what I want with TensorFlow. I haven’t contacted technical support for any issues. Since TensorFlow is vastly documented on the internet, I usually find some good websites where people exchange their views about the solution and apply that."

"It's got quite a big community, which is useful."

More TensorFlow pros

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."

"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."

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

"The solution must provide an efficient LLM."

"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."

"Initially, I faced issues with the solution's configuration."

More Hugging Face cons

"JavaScript is a different thing and all the websites and web apps and all the mobile apps are built-in JavaScript. JavaScript is the core of that. However, TensorFlow is like a machine learning item. What can be improved with TensorFlow is how it can mix in how the JavaScript developers can use TensorFlow."

"In terms of improvement, we always look for ways they can optimize the model, accelerate the speed and the accuracy, and how can we optimize with our different techniques. There are various techniques available in TensorFlow. Maintaining accuracy is an area they should work on."

"I know this is out of the scope of TensorFlow, however, every time I've sent a request, I had to renew the model into RAM and they didn't make that prediction or inference. This makes the point for the request that much longer. If they could provide anything to help in this part, it will be very great."

"Enhancements could include increasing use cases and improving the accuracy of previously built models in TensorFlow. For instance, when we run certain models, the computing power of laptops becomes high."

"TensorFlow deep learning takes a lot of computation power. The more systems you can use, the easier it is. That's a good ability, if you can make a system run immediately at the same time on the same task, it's much faster rather than you having one system running which is slower. Running systems in parallel is a complex situation, but it can improve. There is a lot of work involved."

"I would love to have a user interface like a programming interface. You need to have a set of menus where you can put things together in a graphical interface. The complete automation of the integration of the modules would also be interesting. It’s more like plumbing as opposed to a fully automated environment."

"The solution is hard to integrate with the GPUs."

"It doesn't allow for fast the proto-typing. So usually when we do proto-typing we will start with PyTorch and then once we have a good model that we trust, we convert it into TensorFlow. So definitely, TensorFlow is not very flexible."

More TensorFlow cons

Pricing and Cost Advice

"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."

"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."

"Hugging Face is an open-source solution."

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

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

"I rate TensorFlow's pricing a five out of ten."

"The solution is free."

"We are using the free version."

"I think for learners to deploy a project, you can actually use TensorFlow for free. It's just amazing to have an open-source platform like TensorFlow to deploy your own project. Here in Russia no one really cares about licenses, as it is totally open source and free. My clients in the United States were also pleased to learn when they enquired, that licensing is free."

"I am using the open-source version of TensorFlow and it is free."

"I did not require a license for this solution. It a free open-source solution."

"It is an open-source solution, so anyone can use it free of charge."

"It is open-source software. You don't have to pay all the big bucks like Azure and Databricks."

More TensorFlow pricing and cost advice

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Top Industries

By visitors reading reviews

Computer Software Company

11%

Manufacturing Company

11%

Financial Services Firm

10%

University

10%

Manufacturing Company

15%

Computer Software Company

12%

University

10%

Educational Organization

10%

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

Questions from the Community

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...

What do you like most about TensorFlow?

It empowers us to seamlessly create and deploy machine learning models, offering a versatile solution for implementing sophisticated environments and various types of AI solutions.

What is your experience regarding pricing and costs for TensorFlow?

The solution is free.

What needs improvement with TensorFlow?

The process of creating models could be more user-friendly.

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Product Reports

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AI Development Platforms

November 2024

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October 2024

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Hugging Face

Overview

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

TensorFlow is an open source software library for high performance numerical computation. Its flexible architecture allows easy deployment of computation across a variety of platforms (CPUs, GPUs, TPUs), and from desktops to clusters of servers to mobile and edge devices. Originally developed by researchers and engineers from the Google Brain team within Google’s AI organization, it comes with strong support for machine learning and deep learning and the flexible numerical computation core is used across many other scientific domains.

Sample Customers

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Airbnb, NVIDIA, Twitter, Google, Dropbox, Intel, SAP, eBay, Uber, Coca-Cola, Qualcomm