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: June 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 (2nd)

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 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 PyTorch is 1.6%, up from 1.2% 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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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 valuable features include the ML operations that allow for designing, deploying, testing, and evaluating models."

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

"The solution is easy to scale...The documentation and online community support have been sufficient for us so far."

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

"They are doing a good job of evolving."

"It's user-friendly for business teams as they can understand many aspects through the AWS interface."

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

"SageMaker offers functionalities like Jupyter Notebooks for development, built-in algorithms, model tuning, and options to deploy models on managed infrastructure."

More Amazon SageMaker pros

"The tool is very user-friendly."

"It’s reliable, secure and user-friendly. It allows you to develop any AIML project efficiently. PySearch is the best option for developing any project in the AIML domain. The product is easy to install."

"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."

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

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

"For me, the product's initial setup phase is easy...For beginners, it is fairly easy to learn."

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

"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."

More PyTorch pros

Cons

"I would say the IDE is quite immature, but it is still in its infancy, so I expect it to get better over time."

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

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

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

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

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

"For any cloud provider, the cost has to be substantially reduced, especially in the case of Amazon SageMaker, which is extremely expensive for huge workloads."

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

More Amazon SageMaker cons

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

"PyTorch could make certain things more obvious. Even though it does make things like defining loss functions and calculating gradients in backward propagation clear, these concepts may confuse beginners. We find that it's kind of problematic. Despite having methods called on loss functions during backward passes, the oral documentation for beginners is quite complex."

"I would like a model to be available. I think Google recently released a new version of EfficientNet. It's a really good classifier, and a PyTorch implementation would be nice."

"The training of the models could be faster."

"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."

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

"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

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

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

"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."

"Databricks solution is less costly than Amazon SageMaker."

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

"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."

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

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

More Amazon SageMaker pricing and cost advice

"PyTorch is open-sourced."

"It is free."

"The solution is affordable."

"PyTorch is an open-source solution."

"PyTorch is open source."

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Top Industries

By visitors reading reviews

Financial Services Firm

19%

Computer Software Company

12%

Manufacturing Company

Educational Organization

Manufacturing Company

30%

Financial Services Firm

University

Comms Service Provider

Company Size

By reviewers

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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 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

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Amazon SageMaker vs. PyTorch