Google Vertex AI vs Hugging Face comparison

Read 13 Hugging Face reviews

6,775 Views
6,775 Comparison Views

100% willing to recommend

Google Vertex AI

Comparison Buyer's Guide

Download the report

Executive SummaryUpdated on Apr 20, 2025

Google Vertex AI and Hugging Face are competing products in the machine learning landscape. Google Vertex AI seems to have the upper hand in scalability and data integration, while Hugging Face shines in community-driven innovation and model accessibility.

Features: Google Vertex AI offers seamless integration with Google Cloud services, enhanced scalability for large-scale data operations, and automated ML capabilities. Hugging Face provides an expansive library of pre-trained models, a user-friendly platform for model deployment, and strong collaborative features.

Room for Improvement: Google Vertex AI could improve by offering more open-source model options, better community engagement, and enhanced ease of use for beginners. Hugging Face could benefit from more seamless data integration capabilities, scalability improvements for enterprise use, and enhanced support for real-time data operations.

Ease of Deployment and Customer Service: Vertex AI simplifies deployment with its comprehensive cloud support, making it well-suited for larger enterprises. Its extensive support network adds significant value. Hugging Face offers a more intuitive deployment experience focused on API usage, benefiting both individual developers and agile teams, with community-based support and documentation enhancing the learning experience for developers.

Pricing and ROI: Google Vertex AI's setup demands a higher initial investment but ensures strong ROI through extensive resources and scalability, ideal for large-scale operations. Hugging Face provides affordable entry points with a freemium model, offering attractive ROI for startups and smaller teams seeking to leverage advanced models without high upfront costs.

To learn more, read our detailed Google Vertex AI vs. Hugging Face Report (Updated: June 2025).

Google Vertex AI vs. Hugging Face

June 2025

Download the complete report

Helped 861,524 peers since 2012

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 Vertex AI

Ranking in AI Development Platforms

2nd

Average Rating

8.4

Reviews Sentiment

6.7

Number of Reviews

Ranking in other categories

AI Infrastructure (1st)

Hugging Face

Ranking in AI Development Platforms

3rd

Average Rating

8.2

Reviews Sentiment

7.1

Number of Reviews

Ranking in other categories

No ranking in other categories

Mindshare comparison

As of July 2025, in the AI Development Platforms category, the mindshare of Google Vertex AI is 11.8%, down from 21.3% compared to the previous year. The mindshare of Hugging Face is 12.8%, up from 9.1% compared to the previous year. It is calculated based on PeerSpot user engagement data.

AI Development Platforms

Featured Reviews

Hamada Farag

Technology Consultant at Beta Information Technology

Customization and integration empower diverse AI applications

We are familiar with most Google Cloud services, particularly infrastructure services, storage, compute, AI tools, containerization, GCP containerization, and cloud SQL. We are familiar with approximately eighty percent of Google's services, primarily related to infrastructure, AI, containers, backup, storage, and compute. We are familiar with Gemini AI and Google Vertex AI, and we have completed some exercises and cases with our customers for Google AI. We use automation in machine learning. I work with a team where everyone has specific responsibilities. We have design and development processes in place. Based on my experience, I would rate Google Vertex AI a 9 out of 10.

Read full review

SwaminathanSubramanian

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.

Read full review

Quotes from Members

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

Pros

"Google Vertex AI is an out-of-the-box and very easy-to-use solution."

"We extensively utilize Google Cloud's Vertex AI platform for our machine learning workflows. Specifically, we leverage the IO branch for EDA data in Suresh Live Virtual, employing Forte IT for training machine learning models. The AI model registry in Vertex AI is crucial for cataloging and managing various versions of the models we develop. When it comes to deploying models, we rely on Google Cloud's AI Prediction service, seamlessly integrating it into our workflow for real-time predictions or streaming. For monitoring and tracking the outcomes of model development, we employ Vertex AI Monitoring, ensuring a comprehensive understanding of the model's performance and results. This integrated approach within Vertex AI provides a unified platform for managing, deploying, and monitoring machine learning models efficiently."

"The most valuable feature we've found is the model garden, which allows us to deploy and use various models through the provided endpoints easily."

"Google Vertex AI is better for deployment, configuration, delivery, licensing, and integration compared to other AI platforms."

"The integration of AutoML features streamlines our machine-learning workflows."

"Vertex comes with inbuilt integration with GCP for data storage."

"The most valuable features of the solution are that it is quite flexible, and some of the services are almost low-code, with no-code services, so it gives agents flexibility to build the use cases according to the operational needs."

"It provides the most valuable external analytics."

More Google Vertex AI pros

"I like that Hugging Face is versatile in the way it has been developed."

"Overall, the platform is excellent."

"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."

"It is stable."

"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 product is reliable."

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

"I appreciate the versatility and the fact that it has generalized many models."

More Hugging Face pros

Cons

"It is not completely mature and needs some features and functions. The interface needs to be more user-friendly."

"Google Vertex AI is good in machine learning and AI, but it lacks optimization."

"Both major systems, Azure and Google, are not yet stabilized, especially their customer support."

"The solution is stable, but it is quite slow. Maybe my data is too large, but I think that Google could improve Vertex AI's training time."

"I think the technical documentation is not readily available in the tool."

"I've noticed that using chat activity often presents a broader range of options and insights for a well-constructed question. Improving the knowledge base could be a key aspect for enhancement—expanding the information sources to enhance the generation process."

"The tool's documentation is not good. It is hard."

"It takes a considerable amount of time to process, and I understand the technology behind why it takes this long, but this is something that could be reduced."

More Google Vertex AI cons

"The solution must provide an efficient LLM."

"Regarding scalability, I'm finding the multi-GPU aspect of it challenging."

"Most people upload their pre-trained models on Hugging Face, but more details should be added about the models."

"The initial setup can be rated as a seven out of ten due to occasional issues during model deployment, which might require adjustments."

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

"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."

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

More Hugging Face cons

Pricing and Cost Advice

"The price structure is very clear"

"The Versa AI offers attractive pricing. With this pricing structure, I can leverage various opportunities to bring value to my business. It's a positive aspect worth considering."

"I think almost every tool offers a decent discount. In terms of credits or other stuff, every cloud provider provides a good number of incentives to onboard new clients."

"The solution's pricing is moderate."

"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 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."

See which vendors are best for you

Use our free recommendation engine to learn which AI Development Platforms solutions are best for your needs.

See recommendations

861,524 professionals have used our research since 2012.

Top Industries

By visitors reading reviews

Computer Software Company

13%

Financial Services Firm

12%

Manufacturing Company

Educational Organization

Computer Software Company

10%

Manufacturing Company

10%

University

10%

Financial Services Firm

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

Questions from the Community

What do you like most about Google Vertex AI?

We extensively utilize Google Cloud's Vertex AI platform for our machine learning workflows. Specifically, we leverage the IO branch for EDA data in Suresh Live Virtual, employing Forte IT for trai...

What is your experience regarding pricing and costs for Google Vertex AI?

They have different pricing models like pay-as-you-go or subscription model, and total cost of ownership. It is comparatively cheaper than Azure.

What needs improvement with Google Vertex AI?

Google Vertex AI is one of the best in the market, followed by Azure AI. It can be rated at eight or nine out of ten. It is not completely mature and needs some features and functions. The interfac...

What do you like most about Hugging Face?

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

What needs improvement with Hugging Face?

It is challenging to suggest specific improvements for Hugging Face, as their platform is already very well-organized and efficient. However, they could focus on cleaning up outdated models if they...

What is your primary use case for Hugging Face?

I am working on AI with various large language models for different purposes such as medicine and law, where they are fine-tuned with specific requirements. I download LLMs from Hugging Face for th...

Azure OpenAI vs Google Vertex AI

Comparisons

Compared 34% of the time

Microsoft Azure Machine Learning Studio vs Google Vertex AI

Compared 16% of the time

Amazon SageMaker vs Google Vertex AI

Compared 7% of the time

TensorFlow vs Google Vertex AI

Compared 4% of the time

Amazon Bedrock vs Google Vertex AI

Compared 4% of the time

More Google Vertex AI Competitors

Replicate vs Hugging Face

Compared 31% of the time

GroqCloud Platform vs Hugging Face

Compared 14% of the time

Azure OpenAI vs Hugging Face

Compared 10% of the time

Together Inference vs Hugging Face

Compared 7% of the time

Fireworks AI vs Hugging Face

Compared 6% of the time

More Hugging Face Competitors

Product Reports

Download Google Vertex AI product report

Google Vertex AI

July 2025

Download Hugging Face product report

July 2025

Overview

Build, deploy, and scale ML models faster, with pre-trained and custom tooling within a unified artificial intelligence platform.

Google

Hugging Face offers a platform hosting a wide range of models with efficient natural language processing tools. Known for its open-source nature, comprehensive documentation, and a variety of embedding models, it reduces costs and facilitates easy adoption.

Valued in the tech community for its ability to host diverse models, Hugging Face simplifies tasks in machine learning and artificial intelligence. Users find it easy to fine-tune large language models like LLaMA for custom data training, access a library of open-source models for tailored applications, and utilize options like the Inference API. The platform impresses with its free usage, popularity of trending models, and effective program management, although improvements could be made in security and documentation for more customizable deployments. Collaboration with ecosystem library providers and better model description details could boost its utility.

What are the key features of Hugging Face?

Wide model hosting: Offers a vast array of models for diverse applications.
Inference API: Provides efficient and seamless deployment of models.
Open-source access: Encourages community collaboration and model sharing.
Embedding models: Supports various formats and packages.
Simple platform: User-friendly and cost-effective for businesses.

What benefits can users expect from Hugging Face?

Cost reduction: Access to free or lower-cost tools compared to proprietary software.
Customization: Easy adaptation of pre-trained models to fit specific needs.
Efficiency: Quick deployment and fine-tuning of models for business use.
Community support: Comprehensive documentation and open-source collaboration.

Hugging Face is widely used across industries requiring machine learning solutions, such as creating SQL chatbots or data extraction tools. Organizations focus on fine-tuning language models to enhance business processes and remove reliance on proprietary systems. The platform supports innovative applications, including business-specific AI solutions, demonstrating its flexibility and adaptability.