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

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Executive Summary

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

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January 2026

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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

7.0

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.

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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.

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

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

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

"I have seen a return on investment, probably a factor of four or five."

"They are doing a good job of evolving."

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

"The product aggregates everything we need to build and deploy machine learning models in one place."

"The various integration options available in Amazon SageMaker, such as Firehose for connecting to data pipelines, are simple to use."

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

"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."

"Amazon SageMaker can make it simpler to manage the data flow from start to finish, such as by integrating data, usingthe machine, and deploying models. This process could be more user-friendly compared to other tools. I would also like to improve integration with Bedrock and the LLM connection for AWS."

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

"Having all documentation easily accessible on the front page of SageMaker would be a great improvement."

"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."

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

"The dashboard could be improved by including more features and providing more information about deployed models, their drift, performance, scaling, and customization options."

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

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

"Databricks solution is less costly than Amazon SageMaker."

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

"Amazon SageMaker is a very expensive product."

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

"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."

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

"The tool's pricing is reasonable."

More Amazon SageMaker pricing and cost advice

Information not available

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Top Industries

By visitors reading reviews

Financial Services Firm

18%

Computer Software Company

11%

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	16

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...

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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...

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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.

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Compared 8% of the time

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Compared 6% of the time

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TensorFlow vs Caffe

Compared 62% of the time

PyTorch vs Caffe

Compared 23% of the time

OpenVINO vs Caffe

Compared 15% of the time

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Product Reports

Buyer's Guide

Amazon SageMaker

January 2026

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Buyer's Guide

AI Development Platforms

January 2026

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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)

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

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January 2026

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