Google Vertex AI vs Hugging Face comparison

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Google Vertex AI

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Executive SummaryUpdated on Jul 27, 2025

Google Vertex AI and Hugging Face are both major players in the AI and machine learning sector. Google Vertex AI has the upper hand in corporate settings due to its streamlined integration with Google Cloud, while Hugging Face stands out for its extensive open-source library and adaptability in natural language processing.

Features: Google Vertex AI excels with its strong Google Cloud integration, scalable infrastructure, and comprehensive MLOps tools. It is highly flexible, offering low-code and no-code options for ease of use and features like AutoML for streamlined workflows. Hugging Face is valued for its Transformer library, intuitive model customization, and expansive open-source model collection, which simplifies model testing and deployment.

Room for Improvement: Google Vertex AI could enhance its usability for non-Google environment deployments and improve cost-effectiveness for smaller enterprises. Hugging Face can benefit from stronger enterprise-level support and greater scalability options for larger applications. Both platforms might enhance user experience by further simplifying initial setup procedures and offering richer troubleshooting resources.

Ease of Deployment and Customer Service: Google Vertex AI provides seamless deployment within the Google ecosystem, making it ideal for enterprises using Google Cloud, along with strong customer support. Hugging Face offers a straightforward deployment model focusing on flexibility, backed by an active community rather than traditional support, beneficial for rapid development cycles and community-driven projects.

Pricing and ROI: Google Vertex AI offers competitive pricing suited for businesses already invested in Google services, potentially providing higher ROI through its integrated cloud solutions. Hugging Face is attractive to startups and smaller businesses with its flexible pricing and free-tier options that offer cost efficiency and scalability. Google's pricing is tailored for comprehensive service packages, while Hugging Face focuses on offering accessible and affordable model experimentation options.

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

Google Vertex AI vs. Hugging Face

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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.

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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

"Vertex AI possesses multiple libraries, so it eliminates the need for extensive coding."

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

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

"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."

"It provides the most valuable external analytics."

"The monitoring feature is a true life-saver for data scientists. I give it a ten out of ten."

"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 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."

More Google Vertex AI pros

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

"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 product is reliable."

"Overall, the platform is excellent."

"I would rate this product nine out of ten."

"Hugging Face provides open-source models, making it the best open-source and reliable solution."

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

"There are numerous libraries available, and the documentation is rich and step-by-step, helping us understand which model to use in particular conditions."

More Hugging Face pros

Cons

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

"It would be beneficial to have certain features included in the future, such as image generators and text-to-speech solutions."

"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."

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

"I'm not sure if I have suggestions for improvement."

"I believe that Vertex AI is a robust platform, but its effectiveness depends significantly on the domain knowledge of the developer using it. While Vertex AI does offer support through the console UI in the Google Cloud environment, it is better suited for technical members who have a deeper understanding of machine learning concepts. The platform may be challenging for business process developers (BPDUs) who lack extensive technical knowledge, as it involves intricate customization and handling numerous parameters. Effectively utilizing Vertex AI requires not only familiarity with machine learning frameworks like TensorFlow or PyTorch but also a proficiency in Python programming. The complexity of these requirements might pose challenges for less technically oriented users, making it crucial to have a solid foundation in both machine learning principles and Python coding to extract the full value from Vertex AI. It would be beneficial to have a streamlined process where we can leverage the capabilities of Vertex AI directly through the BigQuery UI. This could involve functionalities such as creating machine learning models within the BigQuery UI, providing a more user-friendly and integrated experience. This would allow users to access and analyze data from BigQuery while simultaneously utilizing Vertex AI to build machine learning models, fostering a more cohesive and efficient workflow."

"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."

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

More Google Vertex AI cons

"It can incorporate AI into its services."

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

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

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

"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."

"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."

More Hugging Face cons

Pricing and Cost Advice

"The price structure is very clear"

"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 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 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."

"The solution is open source."

"So, it's requires expensive machines to open services or open LLM models."

"We do not have to pay for the product."

"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."

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Top Industries

By visitors reading reviews

Computer Software Company

13%

Financial Services Firm

11%

Manufacturing Company

Educational Organization

Computer Software Company

11%

University

10%

Financial Services Firm

Comms Service Provider

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...

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Product Reports

Google Vertex AI

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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.

Google Vertex AI vs. Hugging Face