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Google Cloud AI Platform vs Hugging Face comparison

Google and Hugging Face are both solutions in the AI Development Platforms category. Google is ranked #7 with an average rating of 7.8, while Hugging Face is ranked #5 with an average rating of 8.2. Google holds a 7.3% mindshare in AIDP, compared to Hugging Face’s 7.8% mindshare. Additionally, 100% of Google users are willing to recommend the solution, compared to 100% of Hugging Face users who would recommend it.
Google Cloud AI Platform
Read 8 Google Cloud AI Platform reviews
  • 3,699 Views
  • 2,842 Comparison Views
100% willing to recommend
Hugging Face
Read 10 Hugging Face reviews
  • 4,367 Views
  • 3,787 Comparison Views
100% willing to recommend
 

Comparison Buyer's Guide

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Executive Summary
We performed a comparison between Google Cloud AI Platform and Hugging Face 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.

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To learn more, read our detailed Google Cloud AI Platform vs. Hugging Face Report (Updated: October 2024).
Buyer's Guide
Google Cloud AI Platform vs. Hugging Face
October 2024
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Categories and Ranking

Google Cloud AI Platform
Ranking in AI Development Platforms
7th
Average Rating
7.8
Number of Reviews
8
Ranking in other categories
No ranking in other categories
Hugging Face
Ranking in AI Development Platforms
5th
Average Rating
8.2
Number of Reviews
10
Ranking in other categories
No ranking in other categories
 

Mindshare comparison

As of November 2024, in the AI Development Platforms category, the mindshare of Google Cloud AI Platform is 7.3%, down from 7.5% compared to the previous year. The mindshare of Hugging Face is 7.8%, up from 5.6% compared to the previous year. It is calculated based on PeerSpot user engagement data.
AI Development Platforms
 

Featured Reviews

Vipul-Kumar - PeerSpot reviewer
Nov 3, 2023
An AI platform AI Platform to train your machine learning models at scale, to host your trained model in the cloud, and to use your model to make predictions about new data
It's a host of use cases depending on, again, the the client requirement.  A range of a a wide range of algorithms, EIM voice mails, which can be plugged in right away into your solution into into into our solution, and then have platform that provides know, to to come up with an operational…
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AshishKumar11 - PeerSpot reviewer
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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Quotes from Members

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

Pros

"Some of the valuable features are the vast amount of services that are available, such as load balancer, and the AI architecture."
"The initial setup is very straightforward."
"A range of a a wide range of algorithms, EIM voice mails, which can be plugged in right away into your solution into into into our solution, and then have platform that provides know, to to come up with an operational solution really quick."
"Since the model could be trained in just a couple of hours and deploying it took only a few minutes, the entire process took less than an hour."
"I think the user interface is quite handy, and it is easy to use as compared to the other cloud platforms."
"The solution is able to read 90% of the documents correctly with a 10% error rate."
"On GCP, we are exposing our API services to our clients so that they send us their information. It can be single individual records or it can be a batch of their clients."
"The platform's Google Vision API is particularly valuable."
"It is stable."
"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 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."
"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."
"The solution is easy to use compared to other frameworks like PyTorch and TensorFlow."
"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."
More Hugging Face pros
 

Cons

"The initial setup was straightforward for me but could be difficult for others."
"One thing that I found is that Azure ML does not directly provide you with features on Google Cloud AI Platform, whereas Vertex provides some features of the platform."
"Customizations are very difficult, and they take time."
"The solution can be improved by simplifying the process to make your own models."
"Improvements in text extraction accuracy and pricing adjustments would be helpful."
"It could be more clear, and sometimes there are errors that I don't quite understand."
"At first, there were only the user-managed rules to identify the best attributes of the individual. Then, we came up with a truth set and developed different machine learning models with the help of that truth set, so now it's completely machine learning."
"I think it's the it it also has has evolved quite a bit over the last few years, and Google Cloud folks have been getting, more and more services. But I think from a improvement standpoint, so maybe they can look at adding more algorithms, so adding more AI algorithms to their suite."
"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."
"Initially, I faced issues with the solution's configuration."
"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."
"Most people upload their pre-trained models on Hugging Face, but more details should be added about the models."
"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."
More Hugging Face cons
 

Pricing and Cost Advice

"The pricing is on the expensive side."
"For every thousand uses, it is about four and a half euros."
"The solution has an attractive starting program, which costs only 300 USD for a duration of three months. During this period, one can accomplish a lot of work on the solution."
"The licenses are cheap."
"The price of the solution is competitive."
"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."
"We do not have to pay for the product."
"So, it's requires expensive machines to open services or open LLM models."
"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
Computer Software Company
15%
Financial Services Firm
11%
Manufacturing Company
10%
University
9%
Computer Software Company
11%
Manufacturing Company
11%
Financial Services Firm
10%
University
10%
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
 

Questions from the Community

What do you like most about Google Cloud AI Platform?
A range of a a wide range of algorithms, EIM voice mails, which can be plugged in right away into your solution into into into our solution, and then have platform that provides know, to to come up...
See all answers
What is your experience regarding pricing and costs for Google Cloud AI Platform?
The pricing is on the expensive side.
See all answers
What is your primary use case for Google Cloud AI Platform?
We use Google Cloud AI Platform to extract text from images, such as forms.
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
 

Comparisons

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Compared 24% of the time
Microsoft Azure Machine Learning Studio vs Google Cloud AI Platform Logo
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Compared 14% of the time
Google Vertex AI vs Google Cloud AI Platform Logo
Google Vertex AI vs Google Cloud AI Platform
Compared 10% of the time
Amazon SageMaker vs Google Cloud AI Platform Logo
Amazon SageMaker vs Google Cloud AI Platform
Compared 5% of the time
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Replicate vs Hugging Face Logo
Replicate vs Hugging Face
Compared 47% of the time
Google Vertex AI vs Hugging Face Logo
Google Vertex AI vs Hugging Face
Compared 15% of the time
Azure OpenAI vs Hugging Face Logo
Azure OpenAI vs Hugging Face
Compared 13% of the time
Amazon SageMaker vs Hugging Face Logo
Amazon SageMaker vs Hugging Face
Compared 5% of the time
GroqCloud Platform vs Hugging Face Logo
GroqCloud Platform vs Hugging Face
Compared 3% of the time
More Hugging Face Competitors
 

Product Reports

Buyer's Guide
AI Development Platforms
November 2024
Google Cloud AI Platform reportDownload Google Cloud AI Platform product report
Buyer's Guide
AI Development Platforms
November 2024
Hugging Face reportDownload Hugging Face product report
 

Learn More

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Google
Hugging Face
 

Overview

Google AI Platform is a managed service that enables you to easily build machine learning models, that work on any type of data, of any size. Create your model with the powerful TensorFlow framework that powers many Google products, from Google Photos to Google Cloud Speech.

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

 

Sample Customers

Carousell
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Buyer's Guide
Google Cloud AI Platform vs. Hugging Face
October 2024
Free Report: Google Cloud AI Platform vs. Hugging Face
Find out what your peers are saying about Google Cloud AI Platform vs. Hugging Face and other solutions. Updated: October 2024.
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