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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
11th
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
3rd
Average Rating
8.2
Reviews Sentiment
7.2
Number of Reviews
13
Ranking in other categories
No ranking in other categories
 

Mindshare comparison

As of June 2026, in the AI Development Platforms category, the mindshare of Google Cloud AI Platform is 3.1%, down from 3.9% compared to the previous year. The mindshare of Hugging Face is 4.9%, down from 13.1% compared to the previous year. It is calculated based on PeerSpot user engagement data.
AI Development Platforms Mindshare Distribution
ProductMindshare (%)
Hugging Face4.9%
Google Cloud AI Platform3.1%
Other92.0%
AI Development Platforms
 

Featured Reviews

TJ
Owner at Go knowledge
Streamlines app development with dynamic databases and an easy setup
I used Oracle APEX before Google Cloud AI Platform. Oracle APEX is a free tool, except for the Oracle database, which I can only use with it. To have more freedom, I chose Firebase and Google's solutions as it allows me to run it on a hosted server if I want to.
SwaminathanSubramanian - PeerSpot reviewer
Director/Enterprise Solutions Architect, Technology Advisor at Kyndryl
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

"Some of the valuable features are the vast amount of services that are available, such as load balancer, and the AI architecture."
"We are trying our best to improve our existing models and privacy and to keep on updating it, and also we are trying to use reinforcement learning and separate APIs so that if a user wants to update their data, they can do so."
"I have seen measurable benefits from Google Cloud AI Platform."
"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 feedback left about these tools was really helpful and informative for us"
"I think the user interface is quite handy, and it is easy to use as compared to the other cloud platforms."
"The platform's Google Vision API is particularly valuable."
"The initial setup is very straightforward."
"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."
"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."
"Hugging Face provides open-source models, making it the best open-source and reliable solution."
"Overall, the platform is excellent."
"The product is reliable."
"My preferred aspects are natural language processing and question-answering."
"The solution is easy to use compared to other frameworks like PyTorch and TensorFlow."
"I like that Hugging Face is versatile in the way it has been developed."
 

Cons

"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 model management on Google Cloud AI Platform could be better."
"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."
"The solution can be improved by simplifying the process to make your own models."
"It could be more clear, and sometimes there are errors that I don't quite understand."
"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."
"The initial setup was straightforward for me but could be difficult for others."
"Improvements in text extraction accuracy and pricing adjustments would be helpful."
"Initially, I faced issues with the solution's configuration."
"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."
"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."
"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."
"Access to the models and datasets could be improved. Many interesting ones are restricted."
"It can incorporate AI into its services."
"The solution must provide an efficient LLM."
 

Pricing and Cost Advice

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

By visitors reading reviews
Computer Software Company
13%
Manufacturing Company
11%
Financial Services Firm
10%
University
9%
Comms Service Provider
10%
Financial Services Firm
10%
University
10%
Manufacturing Company
8%
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
By reviewers
Company SizeCount
Small Business5
Midsize Enterprise2
Large Enterprise2
By reviewers
Company SizeCount
Small Business8
Midsize Enterprise2
Large Enterprise4
 

Questions from the Community

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 advice do you have for others considering Google Cloud AI Platform?
I have knowledge of it, and I do recommend Google Cloud AI Platform to other people. I would definitely rate the overall solution as an eight out of ten.
What needs improvement with Hugging Face?
Everything is pretty much sorted in Hugging Face, but it could be improved if there was an AI chatbot or an AI assistant in Hugging Face platform itself, which can guide you through the whole platf...
What is your primary use case for Hugging Face?
My main use case for Hugging Face is to download open-source models and train on a local machine. We use Hugging Face Transformers for simple and fast integration in our applications and AI-based a...
What advice do you have for others considering Hugging Face?
We have seen improved productivity and time saved from using Hugging Face; for a task that would have taken six hours, it saved us five hours, and we completed it in one hour with the plug-and-play...
 

Also Known As

Google Cloud for AI
No data available
 

Overview

 

Sample Customers

Carousell
Information Not Available
Find out what your peers are saying about Google Cloud AI Platform vs. Hugging Face and other solutions. Updated: June 2026.
900,644 professionals have used our research since 2012.