Amazon SageMaker vs Caffe comparison

Amazon Web Services (AWS) and Berkeley Artificial Intelligence Research (BAIR) are both solutions in the AI Development Platforms category. Amazon Web Services (AWS) is ranked #4 with an average rating of 8.1, while Berkeley Artificial Intelligence Research (BAIR) is ranked #27. Amazon Web Services (AWS) holds a 4.0% mindshare in AIDP, compared to Berkeley Artificial Intelligence Research (BAIR)’s 0.9% mindshare. Additionally, 91% of Amazon Web Services (AWS) users are willing to recommend the solution, compared to 100% of Berkeley Artificial Intelligence Research (BAIR) users who would recommend it.

Amazon SageMaker

Read 38 Amazon SageMaker reviews

6,186 Views
2,722 Comparison Views

91% willing to recommend

Caffe

Read 1 Caffe review

326 Views
326 Comparison Views

100% willing to recommend

Amazon SageMaker

Caffe

Comparison Buyer's Guide

Download the report

Executive Summary

We performed a comparison between Amazon SageMaker and Caffe based on real PeerSpot user reviews.

Find out what your peers are saying about Microsoft, Hugging Face, Google and others in AI Development Platforms.

To learn more, read our detailed AI Development Platforms Report (Updated: January 2026).

Buyer's Guide

AI Development Platforms

January 2026

Download the complete report

Helped 881,082 peers since 2012

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

7.0

Number of Reviews

Ranking in other categories

Data Science Platforms (2nd)

Caffe

Ranking in AI Development Platforms

27th

Average Rating

7.0

Reviews Sentiment

6.0

Number of Reviews

Ranking in other categories

No ranking in other categories

Mindshare comparison

As of January 2026, in the AI Development Platforms category, the mindshare of Amazon SageMaker is 4.0%, down from 6.3% compared to the previous year. The mindshare of Caffe is 0.9%, up from 0.2% compared to the previous year. It is calculated based on PeerSpot user engagement data.

AI Development Platforms Market Share Distribution
Product	Market Share (%)
Amazon SageMaker	4.0%
Caffe	0.9%
Other	95.1%

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, such as Firehose for connecting to data pipelines, are simple to use. Tools like AWS Glue integrate well for data transformations. The Databricks integration aids data scientists and engineers. SageMaker is fully managed, offers high availability, flexibility with TensorFlow, PyTorch, and MXNet, and comes with pre-trained algorithms for forecasting, anomaly detection, and more.

Read full review

Raed Lafi

Machine/Deep Learning Engineer at UpWork Freelancer

Speeds up the development process but needs to evolve more to stay relevant

In the future, they should expand text processing, for a recommendation system, or to support some other models as well — that would be great. The concept of Caffe is a little bit complex because it was developed and based in C++. They need to make it easier for a new developer, data scientist, or a new machine or deep learning engineer to understand it. You can't work with metrics and vectors as Python does. Python is a vector-oriented language, but Caffe is not. When you deal with memory in C++, you have to allocate the data you will use in memory. You have to manage everything in C++. Conversely, in Python, you don't need to do that since everything is abstract and done by Python itself. It depends on every use case or your requirement goals. Some clients will require you to use Caffe because maybe their projects are old and they want to continue with Caffe. Others are comfortable with their current situation or they are afraid of migrating to another library. From my point of view, they need to make it easier for a new developer to use it. They should incorporate Python API to make it richer, overall.

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

"The solution's ability to improve work at my organization stems from the ensemble model and a combination of various models it provides."

"The most valuable feature of Amazon SageMaker for me is the model deployment service."

"The most valuable feature of Amazon SageMaker is that you don't have to do any programming in order to perform some of your use cases."

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

"The tool makes our ML model development a bit more efficient because everything is in one environment."

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

"They are doing a good job of evolving."

More Amazon SageMaker pros

"Caffe has helped our company become up-to-date in the market and has helped us speed up the development process of our projects."

Cons

"AI is a new area and AWS needs to have an internship training program available."

"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."

"Improvements are needed in terms of complexity, data security, and access policy integration in Amazon SageMaker."

"The product must provide better documentation."

"The solution is complex to use."

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

"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."

"One area where Amazon SageMaker could improve is its pricing. The high costs can drive companies to explore other cloud options. Additionally, while generally good, the updates sometimes come with bugs, and the documentation could be much better. More examples and clearer guidance would be helpful."

More Amazon SageMaker cons

"The concept of Caffe is a little bit complex because it was developed and based in C++. They need to make it easier for a new developer, data scientist, or a new machine or deep learning engineer to understand it."

Pricing and Cost Advice

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

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

"Databricks solution is less costly than Amazon SageMaker."

"The pricing is comparable."

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

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

"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."

More Amazon SageMaker pricing and cost advice

Information not available

See which vendors are best for you

Use our free recommendation engine to learn which AI Development Platforms solutions are best for your needs.

See recommendations

881,082 professionals have used our research since 2012.

Top Industries

By visitors reading reviews

Financial Services Firm

18%

Computer Software Company

10%

Manufacturing Company

University

No data available

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

By reviewers
Company Size	Count
Small Business	12
Midsize Enterprise	11
Large Enterprise	17

No data available

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...

See all answers

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?

If you manage it effectively, their pricing is reasonable. It's similar to anything in the cloud; if you don't manage it properly, it can be expensive, but if you do, it's fine.

See all answers

Ask a question

Earn 20 points

Comparisons

Databricks vs Amazon SageMaker

Compared 9% of the time

Google Vertex AI vs Amazon SageMaker

Compared 8% of the time

Dataiku vs Amazon SageMaker

Compared 8% of the time

DataRobot vs Amazon SageMaker

Compared 6% of the time

Stack AI vs Amazon SageMaker

Compared 6% of the time

More Amazon SageMaker Competitors

TensorFlow vs Caffe

Compared 53% of the time

PyTorch vs Caffe

Compared 21% of the time

OpenVINO vs Caffe

Compared 15% of the time

IBM Machine Learning vs Caffe

Compared 11% of the time

More Caffe Competitors

Product Reports

Buyer's Guide

Amazon SageMaker

January 2026

Download Amazon SageMaker product report

Buyer's Guide

AI Development Platforms

January 2026

Download Caffe product report

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)

Caffe is a deep learning framework made with expression, speed, and modularity in mind. It is developed by Berkeley AI Research (BAIR) and by community contributors.

Berkeley Artificial Intelligence Research (BAIR)

Sample Customers

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

Information Not Available

Buyer's Guide

AI Development Platforms

January 2026

Download Free Report

Find out what your peers are saying about Microsoft, Hugging Face, Google and others in AI Development Platforms. Updated: January 2026.

DOWNLOAD NOW

881,082 professionals have used our research since 2012.

See our list of best AI Development Platforms vendors.

We monitor all AI Development Platforms reviews to prevent fraudulent reviews and keep review quality high. We do not post reviews by company employees or direct competitors. We validate each review for authenticity via cross-reference with LinkedIn, and personal follow-up with the reviewer when necessary.