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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

"I think the user interface is quite handy, and it is easy to use as compared to the other cloud platforms."
"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."
"Some of the valuable features are the vast amount of services that are available, such as load balancer, and the AI architecture."
"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."
"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 initial setup is very straightforward."
"The platform's Google Vision API is particularly valuable."
"The solution is able to read 90% of the documents correctly with a 10% error rate."
"The solution is easy to use compared to other frameworks like PyTorch and TensorFlow."
"Hugging Face provides open-source models, making it the best open-source and reliable solution."
"There are numerous libraries available, and the documentation is rich and step-by-step, helping us understand which model to use in particular conditions."
"My preferred aspects are natural language processing and question-answering."
"The most valuable features are the inference APIs as it takes me a long time to run inferences on my local machine."
"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."
"I appreciate the versatility and the fact that it has generalized many models."
"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."
 

Cons

"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."
"The model management on Google Cloud AI Platform could be better."
"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."
"The solution can be improved by simplifying the process to make your own models."
"Customizations are very difficult, and they take time."
"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."
"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."
"Access to the models and datasets could be improved."
"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."
"Access to the models and datasets could be improved. Many interesting ones are restricted."
"The solution must provide an efficient LLM."
"It can incorporate AI into its services."
"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."
"Regarding scalability, I'm finding the multi-GPU aspect of it challenging."
 

Pricing and Cost Advice

"For every thousand uses, it is about four and a half euros."
"The licenses are cheap."
"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 price of the solution is competitive."
"The pricing is on the expensive side."
"We do not have to pay for the product."
"So, it's requires expensive machines to open services or open LLM models."
"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."
"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."
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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

Carousell
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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.