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 #5 with an average rating of 8.0, while Hugging Face is ranked #4 with an average rating of 8.2. Amazon Web Services (AWS) holds a 5.6% mindshare in AIDP, compared to Hugging Face’s 13.5% 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

4,140 Views
3,302 Comparison Views

91% willing to recommend

Hugging Face

Read 12 Hugging Face reviews

5,973 Views
4,634 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

5th

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

4th

Average Rating

8.2

Reviews Sentiment

7.0

Number of Reviews

Ranking in other categories

No ranking in other categories

Mindshare comparison

As of April 2025, in the AI Development Platforms category, the mindshare of Amazon SageMaker is 5.6%, down from 8.6% compared to the previous year. The mindshare of Hugging Face is 13.5%, up from 7.6% 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

"The product aggregates everything we need to build and deploy machine learning models in one place."

"The most valuable feature of Amazon SageMaker is its integration. For example, AWS Lambda. Additionally, we can write Python code."

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

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

"We've had no problems with SageMaker's stability."

"They are doing a good job of evolving."

"The tool makes our ML model development a bit more efficient because everything is in one environment."

"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

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

"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."

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

"The product is reliable."

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

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

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

More Hugging Face pros

Cons

"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 needs to be cheaper since it now charges per document for extraction."

"I would recommend having more walkthrough videos and articles beyond AWS Skill Builder."

"In general, improvements are needed on the performance side of the product's graphical user interface-related area since it consumes a lot of time for a user."

"Lacking in some machine learning pipelines."

"In my opinion, one improvement for Amazon SageMaker would be to offer serverless GPUs. Currently, we incur costs on an hourly basis. It would be beneficial if the tool could provide pay-as-you-go pricing based on endpoints."

"I had to create custom templates for labeling multi-data sets, such as text and images, which was time-consuming."

"SageMaker would be improved with the addition of reporting services."

More Amazon SageMaker cons

"It can incorporate AI into its services."

"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."

"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."

"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."

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

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

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

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

More Hugging Face cons

Pricing and Cost Advice

"On average, customers pay about $300,000 USD per month."

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

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

"I would rate the solution's price a ten out of ten since it is very high."

"The cost offers a pay-as-you-go pricing model. It depends on the instance that you do."

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

"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."

"On a scale from one to ten, where one is cheap, and ten is expensive, I rate the solution's pricing a six out of ten."

More Amazon SageMaker pricing and cost advice

"Hugging Face is an open-source solution."

"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."

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

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

"The solution is open source."

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Top Industries

By visitors reading reviews

Financial Services Firm

19%

Educational Organization

13%

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 17% of the time

Azure OpenAI vs Amazon SageMaker

Compared 12% of the time

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Compared 10% of the time

Google Vertex AI vs Amazon SageMaker

Compared 9% of the time

Replicate vs Amazon SageMaker

Compared 3% of the time

More Amazon SageMaker Competitors

Replicate vs Hugging Face

Compared 40% 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 10% of the time

TensorFlow vs Hugging Face

Compared 3% of the time

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Product Reports

Buyer's Guide

Amazon SageMaker

March 2025

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Buyer's Guide

AI Development Platforms

March 2025

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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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