Amazon SageMaker vs Hugging Face comparison

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Amazon SageMaker

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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: June 2025).

Amazon SageMaker vs. Hugging Face

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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 (2nd)

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 June 2025, in the AI Development Platforms category, the mindshare of Amazon SageMaker is 5.3%, down from 8.1% compared to the previous year. The mindshare of Hugging Face is 13.1%, up from 8.6% compared to the previous year. It is calculated based on PeerSpot user engagement data.

AI Development Platforms

Featured Reviews

Saurabh Jaiswal

Python AWS & AI Expert at a tech consulting company

Create innovative assistants with seamless data integration for large-scale projects

The various integration options available in Amazon SageMaker ( /products/amazon-sagemaker-reviews ), such as Firehose for connecting to data pipelines, are simple to use. Tools like AWS Glue ( /products/aws-glue-reviews ) integrate well for data transformations. The Databricks ( /products/databricks-reviews ) integration aids data scientists and engineers. SageMaker is fully managed, offers high availability, flexibility with TensorFlow ( /products/tensorflow-reviews ), PyTorch ( /products/pytorch-reviews ), and MXNet ( /products/mxnet-reviews ), and comes with pre-trained algorithms for forecasting, anomaly detection, and more.

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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 valuable feature of Amazon SageMaker is SageMaker Studio."

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

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

"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 support is very good with well-trained engineers whose training curriculum is rigorous."

"I appreciate the ease of use in 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 these models, making accessing them convenient as needed."

"They are doing a good job of evolving."

More Amazon SageMaker 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."

"I would rate this product nine out of ten."

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

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

"The product is reliable."

"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."

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

More Hugging Face pros

Cons

"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."

"The main challenge with Amazon SageMaker is the integrations."

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

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

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

"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."

"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."

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

More Amazon SageMaker cons

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

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

"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."

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

"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."

"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."

More Hugging Face cons

Pricing and Cost Advice

"The pricing is comparable."

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

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

"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."

"The solution is relatively cheaper."

"Databricks solution is less costly than Amazon SageMaker."

"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."

"I rate the pricing a five on a scale of one to ten, where one is the lowest price, and ten is the highest price. The solution is priced reasonably. There is no additional cost to be paid in excess of the standard licensing fees."

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."

"The solution is open source."

"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."

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Top Industries

By visitors reading reviews

Financial Services Firm

19%

Computer Software Company

12%

Manufacturing Company

Educational Organization

Computer Software Company

11%

Manufacturing Company

10%

Financial Services Firm

10%

University

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...

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...

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

The pricing is high, around an eight. However, SageMaker offers free trials for the first two months, allowing users to determine which features they need. It is considered value for money given it...

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...

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Product Reports

Amazon SageMaker

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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.

Sample Customers

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

Information Not Available

Amazon SageMaker vs. Hugging Face