Google Vertex AI vs Hugging Face comparison

Google and Hugging Face are both solutions in the AI Development Platforms category. Google is ranked #3 with an average rating of 8.2, while Hugging Face is ranked #5 with an average rating of 8.2. Google holds a 23.8% mindshare in AIDP, compared to Hugging Face’s 7.8% mindshare. Additionally, 100% of Google users are willing to recommend the solution, compared to 100% of Hugging Face users who would recommend it.

Google Vertex AI

Read 10 Google Vertex AI reviews

8,236 Views
7,480 Comparison Views

100% willing to recommend

Hugging Face

Read 10 Hugging Face reviews

4,367 Views
3,787 Comparison Views

100% willing to recommend

Google Vertex AI

Hugging Face

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Executive SummaryUpdated on Oct 8, 2024

Google Vertex AI and Hugging Face are leading AI platforms competing in the machine learning model deployment category. Google Vertex AI has the upper hand in deployment ease and customer service, while Hugging Face stands out for its robust features despite a higher price.

Features: Google Vertex AI integrates seamlessly with other Google Cloud services, enhancing user experience for those familiar with these tools. It provides efficient deployment capabilities and strong customer support. Hugging Face offers a comprehensive library of pre-trained models, making it easy to fine-tune and adapt models quickly, and supports extensive model customization.

Room for Improvement: Google Vertex AI can improve model versioning controls to simplify updates, enhance cross-platform compatibility, and expand third-party integrations. Hugging Face could enhance its documentation with more examples for specific applications, improve its pricing transparency, and optimize resource allocation.

Ease of Deployment and Customer Service: Google Vertex AI is praised for a streamlined deployment process due to its excellent customer support, which efficiently addresses issues. Hugging Face deployment is straightforward, but direct customer support is less recognized in user reviews, indicating room for service enhancement.

Pricing and ROI: Google Vertex AI offers competitive pricing, providing good value for its features. Exact pricing is unavailable, but it balances cost and features effectively. Hugging Face is more expensive, justified by its expansive model offerings, indicating a higher initial investment is needed for broader capabilities.

To learn more, read our detailed Google Vertex AI vs. Hugging Face Report (Updated: October 2024).

Buyer's Guide

Google Vertex AI vs. Hugging Face

October 2024

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Categories and Ranking

Google Vertex AI

Ranking in AI Development Platforms

3rd

Average Rating

8.4

Number of Reviews

Ranking in other categories

AI Infrastructure (1st)

Hugging Face

Ranking in AI Development Platforms

5th

Average Rating

8.2

Number of Reviews

Ranking in other categories

No ranking in other categories

Mindshare comparison

As of November 2024, in the AI Development Platforms category, the mindshare of Google Vertex AI is 23.8%, up from 15.9% compared to the previous year. The mindshare of Hugging Face is 7.8%, up from 5.6% compared to the previous year. It is calculated based on PeerSpot user engagement data.

AI Development Platforms

Featured Reviews

Serge Dahdouh

CEO at Les Roches International School of Hotel Management

Aug 16, 2023

A user-friendly platform that automatizes machine learning techniques with minimal effort

I mostly use LLM models on Vertex AI. When there is a large document or multiple documents, I put them in the index database of Vertex AI's platform and it extracts the right information We work with clients who request the implementation of a certain document into a chatbot. Because of the…

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AshishKumar11

Associate Software Engineer at Linkfields Innovations (Pty) Ltd

Jul 25, 2024

Open-sourced, reliable, and enables organizations to finetune data for business requirements

Hugging Face is a website that provides various open-source models. We use them to finetune models for our business. It is just like ChatGPT, but ChatGPT has paid sources. If we have to call an API, we must pay for it. However, Hugging Face has various open-source models like Llama 2 and Llama 3…

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Quotes from Members

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

Pros

"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 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."

"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."

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

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

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

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

More Google Vertex AI pros

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

"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."

"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."

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

More Hugging Face pros

Cons

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

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

"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."

"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."

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

"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."

More Google Vertex AI cons

"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."

"It can incorporate AI into its services."

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

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

"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."

"The solution must provide an efficient LLM."

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

"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."

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."

"The solution's pricing is moderate."

"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 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."

"Hugging Face is an open-source solution."

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

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

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

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Top Industries

By visitors reading reviews

Financial Services Firm

13%

Computer Software Company

13%

Manufacturing Company

Retailer

Computer Software Company

11%

Manufacturing Company

11%

Financial Services Firm

10%

University

10%

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...

See all answers

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

Google Vertex AI is relatively expensive, rating it six for pricing. The pricing model could be more flexible.

See all answers

What needs improvement with Google Vertex AI?

Both major systems, Azure and Google, are not yet stabilized, especially their customer support. It is essential to have a robust customer support system to get quick resolutions as they're frequen...

See all answers

What do you like most about Hugging Face?

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

See all answers

What needs improvement with Hugging Face?

Hugging Face could improve by implementing a search engine or chat bot feature similar to ChatGPT. This would aid developers in easily finding how to fine-tune models with specific data or get mode...

See all answers

What is your primary use case for Hugging Face?

I use Hugging Face primarily to work with open LLM models. I recently started using the open LOM models and also use embedding models. I use these models to train custom data and monitor our deskto...

See all answers

Comparisons

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Compared 9% of the time

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Compared 5% of the time

AWS Machine Learning vs Google Vertex AI

Compared 4% of the time

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Compared 47% of the time

Azure OpenAI vs Hugging Face

Compared 13% of the time

Google Cloud AI Platform vs Hugging Face

Compared 5% of the time

Amazon SageMaker vs Hugging Face

Compared 5% of the time

GroqCloud Platform vs Hugging Face

Compared 3% of the time

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Product Reports

Buyer's Guide

Google Vertex AI

November 2024

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Buyer's Guide

AI Development Platforms

November 2024

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Google

Hugging Face

Overview

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

The AI community building the future. Build, train and deploy state of the art models powered by the reference open source in machine learning.

Buyer's Guide

Google Vertex AI vs. Hugging Face

October 2024

Free Report: Google Vertex AI vs. Hugging Face

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