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 8.0, while Hugging Face is ranked #5 with an average rating of 8.2. Amazon Web Services (AWS) holds a 5.9% mindshare in AIDP, compared to Hugging Face’s 13.4% mindshare. Additionally, 91% 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 36 Amazon SageMaker reviews

4,155 Views
3,372 Comparison Views

91% willing to recommend

Hugging Face

Read 12 Hugging Face reviews

5,656 Views
4,555 Comparison Views

100% willing to recommend

Amazon SageMaker

Hugging Face

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Executive SummaryUpdated on Dec 4, 2024

Amazon SageMaker and Hugging Face compete in the machine learning solutions category. Amazon SageMaker appears to have the upper hand due to its comprehensive suite of tools for all machine learning stages and seamless integration with AWS services, making it appealing for data scientists embedded in the AWS ecosystem.

Features: Amazon SageMaker provides tools such as automated model tuning, seamless API endpoint creation, and flexibility in resource selection. Hugging Face, noted for its open-source models, offers a rich array of pre-trained models and excels in natural language processing, making it easy for users to prototype models with minimal coding.

Room for Improvement: Amazon SageMaker should enhance its ease of use, simplify pricing structures, and improve documentation. There is also room for improvement in scaling for large datasets and better handling big data features. Hugging Face should focus on improving security measures, detail model documentation, and provide customizable deployment options.

Ease of Deployment and Customer Service: Amazon SageMaker supports diverse deployment options, including public, private, and hybrid clouds, offering flexible use cases. However, its technical support is inconsistent and can vary by customer tier. Hugging Face leverages robust documentation and community support, reducing reliance on direct support interactions, although it primarily focuses on public and on-premises deployments.

Pricing and ROI: Amazon SageMaker follows a pay-as-you-go pricing plan, potentially becoming costly without careful resource management. Its cost structure involves compute instances and extra support services, a significant expense for ongoing use. Hugging Face, being largely open-source, offers cost-effective solutions with minimal hosting expenses, although enterprise features might require fees.

To learn more, read our detailed Amazon SageMaker vs. Hugging Face Report (Updated: March 2025).

Buyer's Guide

Amazon SageMaker vs. Hugging Face

March 2025

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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

Amazon SageMaker

Ranking in AI Development Platforms

4th

Average Rating

7.8

Reviews Sentiment

7.1

Number of Reviews

Ranking in other categories

Data Science Platforms (3rd)

Hugging Face

Ranking in AI Development Platforms

5th

Average Rating

8.2

Reviews Sentiment

7.0

Number of Reviews

Ranking in other categories

No ranking in other categories

Mindshare comparison

As of March 2025, in the AI Development Platforms category, the mindshare of Amazon SageMaker is 5.9%, down from 8.7% compared to the previous year. The mindshare of Hugging Face is 13.4%, up from 7.0% compared to the previous year. It is calculated based on PeerSpot user engagement data.

AI Development Platforms

Featured Reviews

Hemant Paralkar

Lead Consultant at Saama

Improves team collaboration with advanced feature sharing but needs a better user experience

Improvement is needed in the no-code and low-code capabilities of Amazon SageMaker. This would empower citizen data scientists to utilize the tool more effectively since many data scientists do not have a core development background. Additionally, dealing with frequent UI updates can be challenging, especially for infrastructure architects like myself. It involves effort to migrate to new UIs, making the updates not seamless. User auditing requires enhancements as tracking operations performed by users can be difficult due to dynamic IP validation and role propagation.

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

"I recommend SageMaker for ML projects if you need to build models from scratch."

"The superb thing that SageMaker brings is that it wraps everything well. It's got the deployment, the whole framework."

"The most valuable features include the ML operations that allow for designing, deploying, testing, and evaluating models."

"They offer insights into everyone making calls in my organization."

"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."

"It's user-friendly for business teams as they can understand many aspects through the AWS interface."

"The most tool's valuable feature, in my experience, is hyperparameter tuning. It allows us to test different parameters for the same model in parallel, which helps us quickly identify the configuration that yields the highest accuracy. This parallel computing capability saves us a lot of time."

"The intuitive interface and streamlined user experience make it easy to navigate and set up various tools like Visual Studio Code or Jupyter Notebook."

More Amazon SageMaker pros

"The product is reliable."

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

"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."

"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."

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

"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 most valuable features are the inference APIs as it takes me a long time to run inferences on my local machine."

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

More Hugging Face pros

Cons

"Creating notebook instances for multiple users is pretty expensive in Amazon SageMaker."

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

"Scalability to handle big data can be improved by making integration with networks such as Hadoop and Apache Spark easier."

"Amazon might need to emphasize its capabilities in generative models more effectively."

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

"The training modules could be enhanced. We had to take in-person training to fully understand SageMaker, and while the trainers were great, I think more comprehensive online modules would be helpful."

"The solution requires a lot of data to train the model."

"The documentation must be made clearer and more user-friendly."

More Amazon SageMaker cons

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

"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."

"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."

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

"The solution must provide an efficient LLM."

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

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

"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

"Amazon SageMaker is a very expensive product."

"The pricing is comparable."

"The tool's pricing is reasonable."

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

"There is no license required for the solution since you can use it on demand."

"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."

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

"The solution is relatively cheaper."

More Amazon SageMaker pricing and cost advice

"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."

"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."

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Top Industries

By visitors reading reviews

Financial Services Firm

19%

Educational Organization

12%

Computer Software Company

11%

Manufacturing Company

Computer Software Company

11%

Financial Services Firm

11%

University

10%

Manufacturing Company

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...

See all answers

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...

See all answers

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

Before deploying SageMaker, I reviewed the pricing, especially for notebook instances. The cost for small to medium instances is not very high.

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?

Access to the models and datasets could be improved. Many interesting ones are restricted. It would be great if they provided access for students or non-professionals who just want to test things.

See all answers

What is your primary use case for Hugging Face?

This is a simple personal project, non-commercial. As a student, that's all I do.

See all answers

Comparisons

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Compared 20% of the time

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Compared 12% of the time

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Compared 9% of the time

Google Vertex AI vs Amazon SageMaker

Compared 9% of the time

Replicate vs Amazon SageMaker

Compared 4% of the time

More Amazon SageMaker Competitors

Replicate vs Hugging Face

Compared 43% of the time

Azure OpenAI vs Hugging Face

Compared 12% of the time

Google Vertex AI vs Hugging Face

Compared 11% of the time

GroqCloud Platform vs Hugging Face

Compared 9% of the time

TensorFlow vs Hugging Face

Compared 3% of the time

More Hugging Face Competitors

Product Reports

Buyer's Guide

Amazon SageMaker

March 2025

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Buyer's Guide

AI Development Platforms

February 2025

Download Hugging Face product report

Also Known As

AWS SageMaker, SageMaker

No data available

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.

Amazon Web Services (AWS)

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.

Hugging Face

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

March 2025

Free Report: Amazon SageMaker vs. Hugging Face

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