Google Vertex AI vs Hugging Face comparison

Read 13 Hugging Face reviews

7,216 Views
7,216 Comparison Views

100% willing to recommend

Google Vertex AI

Comparison Buyer's Guide

Download the report

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: September 2025).

Google Vertex AI vs. Hugging Face

September 2025

Download the complete report

Helped 872,706 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 October 2025, in the AI Development Platforms category, the mindshare of Google Vertex AI is 10.3%, down from 19.5% compared to the previous year. The mindshare of Hugging Face is 11.4%, up from 11.3% 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	10.3%
Hugging Face	11.4%
Other	78.3%

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."

"It provides the most valuable external analytics."

"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."

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

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

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

"The support is perfect and fantastic."

More Google Vertex AI pros

"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 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."

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

"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."

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

"The solution is easy to use compared to other frameworks like PyTorch and TensorFlow."

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

More Hugging Face pros

Cons

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

"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."

"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."

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

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

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

More Google Vertex AI cons

"Hugging Face could improve by implementing a search engine or chat bot feature similar to ChatGPT."

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

"The solution must provide an efficient LLM."

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

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

"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."

"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."

More Hugging Face cons

Pricing and Cost Advice

"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."

"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."

"I recall seeing a fee of nine dollars, and there's also an enterprise option priced at twenty dollars per month."

"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."

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

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

"The solution is open source."

"Hugging Face is an open-source solution."

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

872,706 professionals have used our research since 2012.

Top Industries

By visitors reading reviews

Computer Software Company

14%

Financial Services Firm

10%

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?

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?

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...

Azure OpenAI vs Google Vertex AI

Comparisons

Compared 28% of the time

Microsoft Azure Machine Learning Studio vs Google Vertex AI

Compared 13% of the time

Amazon SageMaker vs Google Vertex AI

Compared 7% of the time

Amazon Bedrock vs Google Vertex AI

Compared 6% of the time

TensorFlow vs Google Vertex AI

Compared 5% of the time

More Google Vertex AI Competitors

Replicate vs Hugging Face

Compared 20% of the time

GroqCloud Platform vs Hugging Face

Compared 16% of the time

Azure OpenAI vs Hugging Face

Compared 10% of the time

Together Inference vs Hugging Face

Compared 8% of the time

TensorFlow vs Hugging Face

Compared 7% of the time

More Hugging Face Competitors

Product Reports

Download Google Vertex AI product report

Google Vertex AI

October 2025

Download Hugging Face product report

October 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.