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 7.9, while Hugging Face is ranked #5 with an average rating of 8.2. Amazon Web Services (AWS) holds a 6.9% mindshare in AIDP, compared to Hugging Face’s 13.2% 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

4,212 Views
3,378 Comparison Views

91% willing to recommend

Hugging Face

Read 10 Hugging Face reviews

4,910 Views
4,171 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: December 2024).

Buyer's Guide

Amazon SageMaker vs. Hugging Face

December 2024

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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

7.1

Number of Reviews

Ranking in other categories

No ranking in other categories

Mindshare comparison

As of January 2025, in the AI Development Platforms category, the mindshare of Amazon SageMaker is 6.9%, down from 8.8% compared to the previous year. The mindshare of Hugging Face is 13.2%, up from 6.3% 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

AshishKumar11

Associate Software Engineer at Linkfields Innovations (Pty) Ltd

Open-sourced, reliable, and enables organizations to finetune data for business requirements

Hugging Face is a website that provides various open-source models. We use them to finetune models for our business. It is just like ChatGPT, but ChatGPT has paid sources. If we have to call an API, we must pay for it. However, Hugging Face has various open-source models like Llama 2 and Llama 3…

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 features in Amazon SageMaker are its AutoML, feature store, and automated hyperparameter tuning capabilities."

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

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

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

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

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

More Amazon SageMaker pros

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

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

"There are numerous libraries available, and the documentation is rich and step-by-step, helping us understand which model to use in particular conditions."

"It is stable."

"The product is reliable."

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

"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."

More Hugging Face pros

Cons

"Lacking in some machine learning pipelines."

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

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

"The platform could be more accessible to users with basic coding skills, making it more intuitive and easier for beginners to use comfortably."

"The pricing of the solution is an issue...In SageMaker, monitoring could be improved by supporting more data types other than JSON and CSV."

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

"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 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."

More Amazon SageMaker cons

"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."

"It can incorporate AI into its services."

"The solution must provide an efficient LLM."

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

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

"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."

"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."

More Hugging Face cons

Pricing and Cost Advice

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

"The support costs are 10% of the Amazon fees and it comes by default."

"Amazon SageMaker is a very expensive product."

"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."

"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 pricing is comparable."

"The tool's pricing is reasonable."

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

More Amazon SageMaker pricing and cost advice

"The solution is open source."

"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."

"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."

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Top Industries

By visitors reading reviews

Financial Services Firm

18%

Educational Organization

14%

Computer Software Company

11%

Manufacturing Company

11%

Computer Software Company

11%

University

10%

Financial Services Firm

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...

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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?

Hugging Face could improve by implementing a search engine or chat bot feature similar to ChatGPT. This would aid developers in easily finding how to fine-tune models with specific data or get mode...

See all answers

What is your primary use case for Hugging Face?

I use Hugging Face primarily to work with open LLM models. I recently started using the open LOM models and also use embedding models. I use these models to train custom data and monitor our deskto...

See all answers

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Compared 49% of the time

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Compared 12% of the time

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Compared 12% of the time

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Compared 5% of the time

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Product Reports

Buyer's Guide

Amazon SageMaker

January 2025

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Buyer's Guide

AI Development Platforms

January 2025

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Also Known As

AWS SageMaker, SageMaker

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Amazon Web Services (AWS)

Hugging Face

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.

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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Buyer's Guide

Amazon SageMaker vs. Hugging Face

December 2024

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