Google Vertex AI vs Hugging Face comparison

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Google Vertex AI

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Executive SummaryUpdated on Nov 2, 2025

Google Vertex AI and Hugging Face are competing platforms in machine learning and AI solutions. Google Vertex AI has an advantage in pricing and support offerings, while Hugging Face's superior features make many users find it worth the investment.

Features: Google Vertex AI integrates seamlessly with Google Cloud services for data processing and model management, offers a comprehensive AI model registry for managing model versions, and provides a highly customizable platform with low-code options. Hugging Face provides a vast library of pre-trained models for natural language processing, a versatile and user-friendly interface that supports multiple model formats, and a rich repository of open-source models and documentation.

Room for Improvement: Google Vertex AI could enhance integration features outside Google's ecosystem, improve NLP capabilities, and refine its pricing structure for smaller businesses. Hugging Face may need better support for cloud integration, more robust customer service, and enhanced features for broader applications beyond NLP.

Ease of Deployment and Customer Service: Google Vertex AI benefits from robust cloud integration, offering seamless deployment for those using Google infrastructure with comprehensive documentation and support. Hugging Face provides a specialized approach featuring user-friendly deployment models for NLP tasks but may require additional configuration for broader applications.

Pricing and ROI: Google Vertex AI offers scalable pricing leveraging Google's cloud infrastructure, potentially resulting in lower setup costs. Hugging Face may have higher initial costs due to specialized features but presents strong ROI through advanced NLP tools.

To learn more, read our detailed AI Development Platforms Report (Updated: October 2025).

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AI Development Platforms

October 2025

Helped 873,808 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

8.2

Reviews Sentiment

6.4

Number of Reviews

Ranking in other categories

AI-Agent Builders (4th)

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 November 2025, in the AI Development Platforms category, the mindshare of Google Vertex AI is 10.0%, down from 18.7% compared to the previous year. The mindshare of Hugging Face is 10.5%, down from 11.9% compared to the previous year. It is calculated based on PeerSpot user engagement data.

AI Development Platforms Market Share Distribution
Product	Market Share (%)
Google Vertex AI	10.0%
Hugging Face	10.5%
Other	79.5%

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

"The most useful function of Google Vertex AI for me is the ease of integration, as we can easily create a prompt and integrate it into our current system."

"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."

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

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

"It provides the most valuable external analytics."

"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."

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

"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."

More Google Vertex AI pros

"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's open-source and has hundreds of packages already available. This makes it quite helpful for creating our LLMs."

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

"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."

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

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

"The product is reliable."

More Hugging Face pros

Cons

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

"Google can improve Google Vertex AI in terms of analysis and accuracy. When passing a very large context, instead of receiving vague responses, it would be better if the system could prompt users not to pass overly large prompts and provide clearer guidance on how to fine-tune Gemini for specific use cases."

"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 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 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."

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

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

"We used AutoML feature for developing AI models automatically, but we are not comfortable with the performance of those models."

More Google Vertex AI cons

"Access to the models and datasets could be improved."

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

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

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

"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."

"Access to the models and datasets could be improved. Many interesting ones are restricted."

"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."

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."

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

"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."

"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."

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Top Industries

By visitors reading reviews

Computer Software Company

14%

Financial Services Firm

Manufacturing Company

Educational Organization

Computer Software Company

10%

University

10%

Financial Services Firm

Comms Service Provider

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

By reviewers
Company Size	Count
Small Business	5
Midsize Enterprise	3
Large Enterprise	7

By reviewers
Company Size	Count
Small Business	8
Midsize Enterprise	2
Large Enterprise	3

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?

I purchased Google Vertex AI directly from Google, as we are a partner of Google. I would rate the pricing for Google Vertex AI as low; the price is affordable.

What needs improvement with Google Vertex AI?

We used AutoML feature for developing AI models automatically, but we are not comfortable with the performance of those models. We have to do some fine-tuning, hyperparameter optimization, and othe...

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

Download Google Vertex AI product report

Google Vertex AI

November 2025

Download Hugging Face product report

November 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.