Amazon SageMaker vs Hugging Face comparison

Amazon Web Services (AWS) and Hugging Face are both solutions in the AI Development Platforms category. Amazon Web Services (AWS) is ranked #4 with an average rating of 7.6, while Hugging Face is ranked #5 with an average rating of 8.2. Amazon Web Services (AWS) holds a 8.1% mindshare in AIDP, compared to Hugging Face’s 7.8% mindshare. Additionally, 89% of Amazon Web Services (AWS) users are willing to recommend the solution, compared to 100% of Hugging Face users who would recommend it.

Amazon SageMaker

Read 29 Amazon SageMaker reviews

4,257 Views
3,388 Comparison Views

89% willing to recommend

Hugging Face

Read 10 Hugging Face reviews

4,367 Views
3,787 Comparison Views

100% willing to recommend

Amazon SageMaker

Hugging Face

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Executive Summary

We performed a comparison between Amazon SageMaker and Hugging Face based on real PeerSpot user reviews.

Find out in this report how the two AI Development Platforms solutions compare in terms of features, pricing, service and support, easy of deployment, and ROI.

To learn more, read our detailed Amazon SageMaker vs. Hugging Face Report (Updated: October 2024).

Buyer's Guide

Amazon SageMaker vs. Hugging Face

October 2024

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Categories and Ranking

Amazon SageMaker

Ranking in AI Development Platforms

4th

Average Rating

7.8

Reviews Sentiment

9.1

Number of Reviews

Ranking in other categories

Data Science Platforms (5th)

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 Amazon SageMaker is 8.1%, down from 8.8% 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

Natu Lauchande

Tech Lead - Sanlam Fintech Cluster - Data,ML,AI Eng. at Sanlam

Feb 27, 2024

Easy to use and manage, but the documentation does not have a lot of information

We use the product for deploying machine learning models. We use it for the machine learning model development process We're currently implementing a project on a cross-selling model. It is like a standard XGBoost model. I’m evaluating the tool to see whether it will improve the workflow.…

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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 of Amazon SageMaker is that you don't have to do any programming in order to perform some of your use cases."

"The most valuable feature of Amazon SageMaker for me is the model deployment service."

"The deployment is very good, where you only need to press a few buttons."

"The few projects we have done have been promising."

"The tool has made client management easier where patients need to upload their health records and we can use the tool to understand details on treatment date, amount, etc."

"The Autopilot feature is really good because it's helpful for people who don't have much experience with coding or data pipelines. When we suggest SageMaker to clients, they don't have to go through all the steps manually. They can leverage Autopilot to choose variables, run experiments, and monitor costs. The results are also pretty accurate."

"The solution's ability to improve work at my organization stems from the ensemble model and a combination of various models it provides."

"Amazon SageMaker is highly valuable for managing ML workloads. It connects to AWS cloud resources, making it easy to deploy algorithms and collaborate using tools like GitLab. It offers a wide range of Python libraries and other necessary tools for modelling and algorithms."

More Amazon SageMaker pros

"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."

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

"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."

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

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

"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 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."

More Hugging Face pros

Cons

"One area where Amazon SageMaker could improve is its pricing. The high costs can drive companies to explore other cloud options. Additionally, while generally good, the updates sometimes come with bugs, and the documentation could be much better. More examples and clearer guidance would be helpful."

"The payment and monitoring metrics are a bit confusing not only for Amazon SageMaker but also for the range of other products that fall under AWS, especially for a new user of the product."

"AI is a new area and AWS needs to have an internship training program available."

"Amazon SageMaker could improve in the area of hyperparameter tuning by offering more automated suggestions and tips during the tuning process."

"There are other better solutions for large data, such as Databricks."

"The user interface (UI) and user experience (UX) of SageMaker and AWS, in general, need improvement as they are not intuitive and require substantial time to learn how to use specific services."

"When starting a new session, the waiting time can be quite long, ranging from two to five minutes."

"I would suggest that Amazon SageMaker provide free slots to allow customers to practice, such as a free slot to try out working with a Sandbox."

More Amazon SageMaker 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."

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

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

"The solution must provide an efficient LLM."

"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."

"It can incorporate AI into its services."

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

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

More Hugging Face cons

Pricing and Cost Advice

"The pricing could be better, especially for querying. The per-query model feels expensive."

"SageMaker is worth the money for our use case."

"The pricing is complicated as it is based on what kind of machines you are using, the type of storage, and the kind of computation."

"Databricks solution is less costly than Amazon SageMaker."

"The support costs are 10% of the Amazon fees and it comes by default."

"The solution is relatively cheaper."

"You don't pay for Sagemaker. You only pay for the compute instances in your storage."

"The tool's pricing is reasonable."

More Amazon SageMaker pricing and cost advice

"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."

"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."

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815,854 professionals have used our research since 2012.

Top Industries

By visitors reading reviews

Financial Services Firm

18%

Educational Organization

14%

Computer Software Company

11%

Manufacturing Company

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

How would you compare Databricks vs Amazon SageMaker?

We researched AWS SageMaker, but in the end, we chose Databricks. Databricks is a Unified Analytics Platform designed to accelerate innovation projects. It is based on Spark so it is very fast. It...

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What do you like most about Amazon SageMaker?

We've had experience with unique ML projects using SageMaker. For example, we're developing a platform similar to ChatGPT that requires models. We utilize Amazon SageMaker to create endpoints for t...

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What is your experience regarding pricing and costs for Amazon SageMaker?

The license cost for Amazon SageMaker ranges between seven thousand to fifteen thousand dollars per month depending on various factors such as the model, amount of data, and geographical locations ...

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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

Databricks vs Amazon SageMaker

Compared 27% of the time

Azure OpenAI vs Amazon SageMaker

Compared 12% of the time

Google Vertex AI vs Amazon SageMaker

Compared 11% of the time

Domino Data Science Platform vs Amazon SageMaker

Compared 7% of the time

DataRobot vs Amazon SageMaker

Compared 4% of the time

More Amazon SageMaker Competitors

Replicate vs Hugging Face

Compared 47% of the time

Google Vertex AI vs Hugging Face

Compared 15% 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

GroqCloud Platform vs Hugging Face

Compared 3% of the time

More Hugging Face Competitors

Product Reports

Buyer's Guide

Amazon SageMaker

November 2024

Download Amazon SageMaker product report

Buyer's Guide

AI Development Platforms

November 2024

Download Hugging Face product report

Also Known As

AWS SageMaker, SageMaker

No data available

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Amazon Web Services (AWS)

Hugging Face

Overview

Amazon SageMaker is a fully-managed platform that enables developers and data scientists to quickly and easily build, train, and deploy machine learning models at any scale. Amazon SageMaker removes all the barriers that typically slow down developers who want to use machine learning.

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

Sample Customers

DigitalGlobe, Thomson Reuters Center for AI and Cognitive Computing, Hotels.com, GE Healthcare, Tinder, Intuit

Information Not Available

Buyer's Guide

Amazon SageMaker vs. Hugging Face

October 2024

Free Report: Amazon SageMaker vs. Hugging Face

Find out what your peers are saying about Amazon SageMaker vs. Hugging Face and other solutions. Updated: October 2024.

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