Amazon SageMaker vs PyTorch comparison

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Amazon SageMaker

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Executive SummaryUpdated on Dec 4, 2024

Amazon SageMaker and PyTorch compete in the machine learning category. Amazon SageMaker has the upper hand due to its extensive integration with AWS services and comprehensive platform capabilities.

Features: Amazon SageMaker offers Random Cut Forest, integration with AWS services, SageMaker Studio, AutoML, and model deployment. PyTorch is recognized for its easy-to-use approach, strong community support, backward compatibility, and custom deep learning model capabilities.

Room for Improvement: Amazon SageMaker users seek improvements in IDE maturity, simplified pricing, expanded use cases, intuitive interface, better documentation, and lower costs for large workloads. PyTorch could improve in automation, stability, parallel processing, and documentation clarity for beginners.

Ease of Deployment and Customer Service: Amazon SageMaker deploys mainly on public cloud environments with optional on-premises setups, leveraging AWS integration. Users report good support but note its high cost and varied responsiveness. PyTorch offers flexible deployment options in both cloud and on-premises environments. Its community resources provide satisfactory support, reducing the need for direct assistance.

Pricing and ROI: Amazon SageMaker pricing is based on compute resources, with extra fees for support services. Despite being costly, it offers considerable ROI for specific use cases due to its lifecycle management and AWS integration. PyTorch, being open-source and free, is a cost-effective alternative for deep learning without the necessity for an integrated platform.

To learn more, read our detailed Amazon SageMaker vs. PyTorch Report (Updated: April 2025).

Amazon SageMaker vs. PyTorch

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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)

PyTorch

Ranking in AI Development Platforms

7th

Average Rating

8.6

Reviews Sentiment

7.2

Number of Reviews

Ranking in other categories

No ranking in other categories

Mindshare comparison

As of May 2025, in the AI Development Platforms category, the mindshare of Amazon SageMaker is 5.5%, down from 8.5% compared to the previous year. The mindshare of PyTorch is 1.5%, up from 1.3% 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.

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Rohan Sharma

AI/ML Co-Lead at Developer Student Clubs - GGV

Enabled creation of innovative projects through developer-friendly features

The aspect I like most about PyTorch is that it is really developer-friendly. Developers can constantly create new things, and everyone around the world can use it for free because it's an open-source product. What I personally like is that PyTorch has enabled users to use Apple's M1 chip natively for GPU users. Unlike other libraries using CUDA, PyTorch utilizes Metal Performance Shaders (MPS) to enable GPU usage on M1 chips.

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Quotes from Members

We asked business professionals to review the solutions they use. Here are some excerpts of what they said:

Pros

"The most tool's valuable feature, in my experience, is hyperparameter tuning. It allows us to test different parameters for the same model in parallel, which helps us quickly identify the configuration that yields the highest accuracy. This parallel computing capability saves us a lot of time."

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

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

"I recommend SageMaker for ML projects if you need to build models from scratch."

"One of the most valuable features of Amazon SageMaker for me is the one-touch deployment, which simplifies the process greatly."

"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."

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

"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."

More Amazon SageMaker pros

"The product's initial setup phase is easy."

"The tool is very user-friendly."

"PyTorch allows me to build my projects from scratch."

"yTorch is gaining credibility in the research space, it's becoming easier to find examples of papers that use PyTorch. This is an advantage for someone who uses PyTorch primarily."

"Its interface is the most valuable. The ability to have an interface to train machine learning models and construct them with the high-level interface, without excess busting and reconstructing the same technical elements, is very useful."

"It's been pretty scalable in terms of using multiple GPUs."

"PyTorch is developer-friendly, allowing developers to continuously create new projects."

"I like that PyTorch actually follows the pythonic way, and I feel that it's quite easy. It's easy to find compared to others who require us to type a long paragraph of code."

More PyTorch 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."

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

"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."

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

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

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

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

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

More Amazon SageMaker cons

"I've had issues with stability when I use a lot of data and try out different combinations of modeling techniques."

"PyTorch needs improvement in working on ARM-based chips. They have unified memory for GPU and RAM, however, current GPUs used for processing are slow."

"The product has breakdowns when we change the versions a lot."

"On the production side of things, having more frameworks would be helpful."

"The analyzing and latency of compiling could be improved to provide enhanced results."

"The training of the models could be faster."

"I do not have any complaints."

"There is not enough documentation about some methods and parameters. It is sometimes difficult to find information."

More PyTorch cons

Pricing and Cost Advice

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

"The solution is relatively cheaper."

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

"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."

"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."

"Amazon SageMaker is a very expensive product."

"Databricks solution is less costly than Amazon SageMaker."

More Amazon SageMaker pricing and cost advice

"It is free."

"The solution is affordable."

"PyTorch is open-sourced."

"PyTorch is an open-source solution."

"PyTorch is open source."

"It is free."

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Top Industries

By visitors reading reviews

Financial Services Firm

19%

Educational Organization

11%

Computer Software Company

11%

Manufacturing Company

30%

Computer Software Company

10%

Financial Services Firm

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?

Before deploying SageMaker, I reviewed the pricing, especially for notebook instances. The cost for small to medium instances is not very high.

What do you like most about PyTorch?

The tool is very user-friendly.

What is your experience regarding pricing and costs for PyTorch?

I haven't gone for a paid plan yet. I've just been using the free trial or open-source version.

What needs improvement with PyTorch?

PyTorch needs improvement in working on ARM-based chips. Although they have unified memory for GPU and RAM, they are unable to utilize these GPUs for processing efficiently. They take so much time....

Databricks vs Amazon SageMaker

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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)

We've built this course as an introduction to deep learning. Deep learning is a field of machine learning utilizing massive neural networks, massive datasets, and accelerated computing on GPUs. Many of the advancements we've seen in AI recently are due to the power of deep learning. This revolution is impacting a wide range of industries already with applications such as personal voice assistants, medical imaging, automated vehicles, video game AI, and more.

In this course, we'll be covering the concepts behind deep learning and how to build deep learning models using PyTorch. We've included a lot of hands-on exercises so by the end of the course, you'll be defining and training your own state-of-the-art deep learning models.

Sample Customers

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

Information Not Available

Amazon SageMaker vs. PyTorch