Try our new research platform with insights from 80,000+ expert users

Amazon Comprehend vs Microsoft Azure Machine Learning Studio comparison

 

Comparison Buyer's Guide

Executive Summary

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 Comprehend
Ranking in Data Science Platforms
19th
Average Rating
8.0
Reviews Sentiment
7.4
Number of Reviews
2
Ranking in other categories
No ranking in other categories
Microsoft Azure Machine Lea...
Ranking in Data Science Platforms
4th
Average Rating
7.6
Reviews Sentiment
7.0
Number of Reviews
61
Ranking in other categories
AI Development Platforms (3rd)
 

Mindshare comparison

As of April 2025, in the Data Science Platforms category, the mindshare of Amazon Comprehend is 0.5%, down from 0.8% compared to the previous year. The mindshare of Microsoft Azure Machine Learning Studio is 5.3%, down from 9.4% compared to the previous year. It is calculated based on PeerSpot user engagement data.
Data Science Platforms
 

Featured Reviews

Ashish Lata - PeerSpot reviewer
Integration with automation tools enhances customer sentiment analysis
Comprehend is a useful service for sentiment analysis as it analyzes customer transcripts to evaluate interactions between customers and agents. It provides scores indicating whether sentiments are positive, negative, or neutral. The integration with AWS services like DynamoDB and Lambda facilitates automated analysis, contributing to more informed assessments of customer interactions.
Takayuki Umehara - PeerSpot reviewer
Streamlined workflows with drag and drop convenience but needs enhancements in AI
I use Machine Learning Studio for system reselling and integration Machine Learning Studio is easy to use, with a significant feature being the drag and drop interface that enhances workflow without any complaints. It provides a return on investment and cost savings, proving beneficial for…

Quotes from Members

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

Pros

"I am totally happy with AWS support, as they provide excellent solutions."
"Amazon Comprehend works with a large pool of doctors. They're building the product based on working with domain experts."
"The solution is scalable."
"It's easy to use."
"Microsoft Azure Machine Learning Studio is easy to use and deploy."
"The product's standout feature is a robust multi-file network with limited availability."
"Anyone who isn't a programmer his whole life can adopt it. All he needs is statistics and data analysis skills."
"Its ability to publish a predictive model as a web based solution and integrate R and python codes are amazing."
"The most valuable feature is its compatibility with Tensorflow."
"The notebook feature allows you to write inquiries and create dashboards. These dashboards can integrate with multiple databases, such as Excel, HANA, or SQL Server."
 

Cons

"It is a bit complex to scale. It is still evolving as a product."
"There is room for improvement in terms of accuracy. For example, when a sentence expresses a negative sentiment, such as 'I want to cancel my credit card,' it is crucial for the system to accurately identify it as negative."
"Technical support could improve their turnaround time."
"I rate the support from Microsoft as five out of ten. It could be improved."
"n the solution, there is the concept of workspaces, and there is no means to share the computing infrastructure across those workspaces."
"It is not easy. It is a complex solution. It takes some time to get exposed to all the concepts. We're trying to have a CI/CD pipeline to deploy a machine learning model using negative actions. It was not easy. The components that we're using might have something to do with this."
"I think they should improve two things. They should make their user interface more user-friendly. Integration could also be better. Because Microsoft Machine Learning is a Microsoft product, it's fully integrated with Microsoft Azure but not fully supported for other platforms like IBM or AWS or something else."
"Microsoft Azure Machine Learning Studio worked okay for me, so right now, I don't have any room for improvement in mind for it. What I'd like added to Microsoft Azure Machine Learning Studio in its next version is a categorization for use cases or a template that makes the use cases simple to map out, for example, for healthcare, medical, or finance use cases, etc. This would be very helpful for users of Microsoft Azure Machine Learning Studio, especially for beginners."
"If you want to be able to deploy your tools outside of Microsoft Azure, this is not the best choice."
"It would be great if the solution integrated Microsoft Copilot, its AI helper."
 

Pricing and Cost Advice

Information not available
"To use MLS is fairly cheap. Even the paid account is something like $20/month, unless you are provisioning large numbers of VMs for a Hadoop cluster. The main MS makes money with this solution is forcing the user to deploy their model on REST API, and being charged each time the API is accessed. There are several pricing tiers for the API. If you do not use the API, then value of MLS is to create rapid experiments ($20/month). The resulting model is not exportable to use, thus you’ll have to recreate the algorithms in either R or Python, which is what I did. MLS results gave me a direction to work with, the actual work is mostly done in R and Python outside of MLS."
"The solution operates on a pay-per-use model."
"I would rate the pricing an eight out of ten, with ten being very expensive. Not very expensive, not very cheap."
"The licensing cost is very cheap. It's less than $50 a month."
"From a developer's perspective, I find the price of this solution high."
"The product's pricing is reasonable."
"I am paying for it following a pay-as-you-go. So, the more I use it, the more it costs."
"I used the free student license for a few months to operate the solution, but I'll have to pay for it if I want to do more now."
report
Use our free recommendation engine to learn which Data Science Platforms solutions are best for your needs.
845,040 professionals have used our research since 2012.
 

Top Industries

By visitors reading reviews
No data available
Financial Services Firm
13%
Computer Software Company
11%
Manufacturing Company
10%
Healthcare Company
7%
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
No data available
 

Questions from the Community

What needs improvement with Amazon Comprehend?
Regarding improvements, I would focus on accuracy. For example, if a customer says, 'I want to cancel my credit card,' it should clearly be identified as a negative sentiment. Improving accuracy in...
What is your primary use case for Amazon Comprehend?
I have used Amazon Comprehend primarily for sentiment analysis in my project. I analyze customer transcripts to determine if they are satisfied with the agents they interact with. I store the trans...
What advice do you have for others considering Amazon Comprehend?
I would rate Amazon Comprehend an eight out of ten because there is always room for improvement, especially in terms of accuracy. For those new to Comprehend, understanding its usage and reviewing ...
Which do you prefer - Databricks or Azure Machine Learning Studio?
Databricks gives you the option of working with several different languages, such as SQL, R, Scala, Apache Spark, or Python. It offers many different cluster choices and excellent integration with ...
What do you like most about Microsoft Azure Machine Learning Studio?
The learning curve is very low. Operationalizing the model is also very easy within the Azure ecosystem.
What is your experience regarding pricing and costs for Microsoft Azure Machine Learning Studio?
Pricing is considered to be top-segment and should be improved. I rate the pricing as three or four on a scale of one to ten in terms of affordability.
 

Also Known As

No data available
Azure Machine Learning, MS Azure Machine Learning Studio
 

Overview

 

Sample Customers

LexisNexis, Vibes, FINRA, VidMob
Walgreens Boots Alliance, Schneider Electric, BP
Find out what your peers are saying about Amazon Comprehend vs. Microsoft Azure Machine Learning Studio and other solutions. Updated: March 2025.
845,040 professionals have used our research since 2012.