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 (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

"I have contacted the solution's technical support, and they were really good. I rate the technical support a ten out of ten."

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

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

"SageMaker is a comprehensive platform where I can perform all machine learning activities."

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

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

"The feature I found most valuable is the data catalog, as it assists with the lineage of data through the preparation pipeline."

More Amazon SageMaker pros

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

"Overall, the platform is excellent."

"The product is reliable."

"It is stable."

"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."

"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."

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

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

More Hugging Face pros

Cons

"The solution is complex to use."

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

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

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

"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."

"One area for improvement is the pricing, which can be quite high."

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

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

More Amazon SageMaker cons

"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."

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

"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."

"It can incorporate AI into its services."

"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."

"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."

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

More Hugging Face cons

Pricing and Cost Advice

"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."

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

"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."

"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."

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

"The product is expensive."

"The pricing is comparable."

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

More Amazon SageMaker pricing and cost advice

"The solution is open source."

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

"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."

"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%

Manufacturing Company

10%

University

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

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