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

June 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

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 July 2025, in the AI Development Platforms category, the mindshare of Amazon SageMaker is 5.3%, down from 7.6% compared to the previous year. The mindshare of Hugging Face is 12.8%, up from 9.1% 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.

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

"Feature Store, CodeCommit, versioning, model control, and CI/CD pipelines are the most valuable features in Amazon SageMaker."

"The evolution from SageMaker Classic to SageMaker Studio, particularly the UI part of Studio, is commendable."

"I appreciate the ease of use in Amazon SageMaker."

"SageMaker supports building, training, and deploying AI models from scratch, which is crucial for my ML project."

"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."

"The most valuable feature of Amazon SageMaker is SageMaker Studio."

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

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

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

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

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

"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."

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

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

More Hugging Face pros

Cons

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

"The model repository is a concern as models are stored on a bucket and there's an issue with versioning."

"There is room for improvement in the collaboration with serverless architecture, particularly integration with AWS Lambda."

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

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

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

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

More Amazon SageMaker cons

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

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

"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."

"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."

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

More Hugging Face cons

Pricing and Cost Advice

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

"Databricks solution is less costly than Amazon SageMaker."

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

"The tool's pricing is reasonable."

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

"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."

"In terms of pricing, I'd also rate it ten out of ten because it's been beneficial compared to other solutions."

"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

"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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Top Industries

By visitors reading reviews

Financial Services Firm

19%

Computer Software Company

12%

Manufacturing Company

Educational Organization

Computer Software Company

10%

University

10%

Manufacturing Company

10%

Financial Services Firm

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

Download Amazon SageMaker product report

Amazon SageMaker

July 2025

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July 2025

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

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