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Amazon SageMaker vs Darwin comparison

 

Comparison Buyer's Guide

Executive SummaryUpdated on Dec 5, 2024

Review summaries and opinions

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

ROI

Sentiment score
7.3
Amazon SageMaker delivers high ROI by reducing costs and time, often providing returns multiple times the initial investment.
Sentiment score
6.2
Companies find Darwin efficient, preventing revenue loss and enhancing machine learning, with returns two to three times higher.
The return on investment varies by use case and offers significant value in revenue increases and cost saving capabilities, especially in real time fraud detection and targeted advertisements.
 

Customer Service

Sentiment score
7.2
Amazon SageMaker support is generally praised, but service quality varies; premium customers receive better, more responsive assistance.
Sentiment score
8.4
Darwin's support is highly responsive and efficient, quickly resolving issues and providing valuable guidance, ensuring customer satisfaction.
The technical support from AWS is excellent.
The response time is generally swift, usually within seven to eight hours.
The support is very good with well-trained engineers.
 

Scalability Issues

Sentiment score
7.6
Amazon SageMaker offers scalability and adaptability across enterprises, but GPU limitations and user skills impact its overall efficiency.
Sentiment score
6.7
Darwin scales well with challenges on large datasets; plans for expansion need internal changes for wider departmental adoption.
The availability of GPU instances can be a challenge, requiring proper planning.
It works very well with large data sets from one terabyte to fifty terabytes.
Amazon SageMaker is scalable and works well from an infrastructure perspective.
 

Stability Issues

Sentiment score
7.8
Amazon SageMaker is stable with high reliability, especially when properly configured, and minor glitches do not significantly impact performance.
Sentiment score
7.0
Darwin's stability has improved, boasting 99% availability, though some issues persist; support is responsive, yet enhancements continue.
There are issues, but they are easily detectable and fixable, with smooth error handling.
I rate the stability of Amazon SageMaker between seven and eight.
 

Room For Improvement

Users seek better pricing, interface, integration, documentation, AI, dataset support, security, serverless options, and AWS collaboration.
Darwin users seek API integration, improved functionality, educational resources, and better automation for precision and transparency in AI processes.
Integration of the latest machine learning models like the new Amazon LLM models could enhance its capabilities.
This would empower citizen data scientists to utilize the tool more effectively since many data scientists do not have a core development background.
Having all documentation easily accessible on the front page of SageMaker would be a great improvement.
 

Setup Cost

Amazon SageMaker offers flexible, competitive pricing but can be costly, with value varying by user, plus available discounts.
Darwin's licensing costs are significant yet often seen as valuable, with predictable setup fees and optional costs for integrations.
The cost for small to medium instances is not very high.
The pricing is high, around an eight.
The pricing can be up to eight or nine out of ten, making it more expensive than some cloud alternatives yet more economical than on-premises setups.
 

Valuable Features

Amazon SageMaker offers comprehensive tools for end-to-end machine learning, including model deployment, scalability, and user-friendly features.
Darwin excels in data cleaning, model-building, and integration, enhancing productivity and accessibility for non-experts in machine learning.
The most valuable features include the ML operations that allow for designing, deploying, testing, and evaluating models.
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.
SageMaker supports building, training, and deploying AI models from scratch, which is crucial for my ML project.
 

Categories and Ranking

Amazon SageMaker
Ranking in Data Science Platforms
3rd
Average Rating
7.8
Reviews Sentiment
7.1
Number of Reviews
37
Ranking in other categories
AI Development Platforms (5th)
Darwin
Ranking in Data Science Platforms
27th
Average Rating
8.0
Reviews Sentiment
6.7
Number of Reviews
8
Ranking in other categories
No ranking in other categories
 

Mindshare comparison

As of May 2025, in the Data Science Platforms category, the mindshare of Amazon SageMaker is 6.9%, down from 9.7% compared to the previous year. The mindshare of Darwin is 0.3%, up from 0.3% compared to the previous year. It is calculated based on PeerSpot user engagement data.
Data Science Platforms
 

Featured Reviews

Saurabh Jaiswal - PeerSpot reviewer
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.
AC
Empowers SMEs to build solutions and interface them with the existing business systems, products and workflows.
There's always room for improvement in the UI and continuing to evolve it to do everything that the rest of AI can do. Because it's so much better than traditional methods, we don't get a ton of complaints of, "Oh, we wish we could do that." Most people are happy to see that they can build models that quickly, and that it can be done by the people who actually understand the problem, i.e. SMEs, rather than having to rely on data scientists. There's a small learning curve, but it's shorter for an SME in a given industry to learn Darwin than it takes for data scientists to learn industry-specific problems. The industry I work in deals with tons and tons of data and a lot of it lends itself to Darwin-created solutions. Initially, there were some limitations around the size of the datasets, the number of rows and number of columns. That was probably the biggest challenge. But we've seen the Darwin product, over time, slowly remove those limitations. We're happy with the progress they've made.
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Top Industries

By visitors reading reviews
Financial Services Firm
19%
Educational Organization
11%
Computer Software Company
11%
Manufacturing Company
9%
No data available
 

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.
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Also Known As

AWS SageMaker, SageMaker
No data available
 

Overview

 

Sample Customers

DigitalGlobe, Thomson Reuters Center for AI and Cognitive Computing, Hotels.com, GE Healthcare, Tinder, Intuit
Hunt Oil, Hitachi High-Tech Solutions
Find out what your peers are saying about Amazon SageMaker vs. Darwin and other solutions. Updated: April 2025.
850,236 professionals have used our research since 2012.