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Google Cloud AI Platform vs Hugging Face comparison

 

Comparison Buyer's Guide

Executive SummaryUpdated on Dec 4, 2024

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

Google Cloud AI Platform
Ranking in AI Development Platforms
7th
Average Rating
7.8
Reviews Sentiment
7.1
Number of Reviews
9
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
12
Ranking in other categories
No ranking in other categories
 

Mindshare comparison

As of February 2025, in the AI Development Platforms category, the mindshare of Google Cloud AI Platform is 4.9%, down from 7.0% compared to the previous year. The mindshare of Hugging Face is 13.2%, up from 6.6% compared to the previous year. It is calculated based on PeerSpot user engagement data.
AI Development Platforms
 

Featured Reviews

Vipul-Kumar - PeerSpot reviewer
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
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.
SwaminathanSubramanian - PeerSpot reviewer
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.

Quotes from Members

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

Pros

"The initial setup is very straightforward."
"The feedback left about these tools was really helpful and informative for us"
"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."
"I have seen measurable benefits from Google Cloud AI Platform."
"The solution is able to read 90% of the documents correctly with a 10% error rate."
"I think the user interface is quite handy, and it is easy to use as compared to the other cloud platforms."
"Some of the valuable features are the vast amount of services that are available, such as load balancer, and the AI architecture."
"The product is reliable."
"I like that Hugging Face is versatile in the way it has been developed."
"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."
"My preferred aspects are natural language processing and question-answering."
"There are numerous libraries available, and the documentation is rich and step-by-step, helping us understand which model to use in particular conditions."
"I appreciate the versatility and the fact that it has generalized many models."
"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."
 

Cons

"The solution can be improved by simplifying the process to make your own models."
"The technical support from Google is not very fast. I think it is about a five out of ten even though they have courses online where I can learn a lot, if I really need support, I have to wait a very long time."
"The initial setup was straightforward for me but could be difficult for others."
"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."
"It could be more clear, and sometimes there are errors that I don't quite understand."
"Customizations are very difficult, and they take time."
"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."
"Improvements in text extraction accuracy and pricing adjustments would be helpful."
"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."
"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."
"It can incorporate AI into its services."
"Regarding scalability, I'm finding the multi-GPU aspect of it challenging."
"The solution must provide an efficient LLM."
"Access to the models and datasets could be improved."
"Hugging Face could improve by implementing a search engine or chat bot feature similar to ChatGPT."
"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."
 

Pricing and Cost Advice

"For every thousand uses, it is about four and a half euros."
"The price of the solution is competitive."
"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 pricing is on the expensive side."
"The solution is open source."
"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."
"I recall seeing a fee of nine dollars, and there's also an enterprise option priced at twenty dollars per month."
"Hugging Face is an open-source solution."
"We do not have to pay for the product."
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Top Industries

By visitors reading reviews
Computer Software Company
15%
Manufacturing Company
11%
Financial Services Firm
10%
University
8%
Computer Software Company
11%
Manufacturing Company
11%
University
10%
Financial Services Firm
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...
What is your experience regarding pricing and costs for Google Cloud AI Platform?
For the most part, the pricing is perfect sinceit grows with the use of my app. In some cases, they could be more specific about the pricing, especially for some AI features.
What is your primary use case for Google Cloud AI Platform?
I use Google Cloud AI Platform due to Firebase and the many APIs that are available with it.
What do you like most about Hugging Face?
My preferred aspects are natural language processing and question-answering.
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.
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.
 

Overview

 

Sample Customers

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Find out what your peers are saying about Google Cloud AI Platform vs. Hugging Face and other solutions. Updated: January 2025.
837,501 professionals have used our research since 2012.