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

Amazon Comprehend vs Dataiku 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
22nd
Average Rating
8.0
Reviews Sentiment
7.4
Number of Reviews
2
Ranking in other categories
No ranking in other categories
Dataiku
Ranking in Data Science Platforms
4th
Average Rating
8.2
Reviews Sentiment
6.5
Number of Reviews
20
Ranking in other categories
No ranking in other categories
 

Mindshare comparison

As of March 2026, in the Data Science Platforms category, the mindshare of Amazon Comprehend is 0.8%, up from 0.5% compared to the previous year. The mindshare of Dataiku is 6.7%, down from 12.5% compared to the previous year. It is calculated based on PeerSpot user engagement data.
Data Science Platforms Mindshare Distribution
ProductMindshare (%)
Dataiku6.7%
Amazon Comprehend0.8%
Other92.5%
Data Science Platforms
 

Featured Reviews

Ashish Lata - PeerSpot reviewer
Professional Freelancer at Open for all
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.
SK
Senior Data Scientist at Deloitte
Visual workflows have streamlined healthcare analytics and have reduced reporting time significantly
In terms of improvement, I cannot comment on the LLMs or the agentic view as I have not used them yet. However, I feel that better documentation is necessary. Dataiku should establish a stronger community since this is proprietary software, where users can share knowledge. Although they have some community interaction, it is often challenging to find assistance when stuck. For example, when I was new to Dataiku and trying to use an external optimization tool such as CPLEX, I struggled with resource directory linking to a project's notebook. Detailed documentation and community discussions could have significantly alleviated these issues for users such as myself.

Quotes from Members

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

Pros

"Amazon Comprehend works with a large pool of doctors. They're building the product based on working with domain experts."
"I am totally happy with AWS support, as they provide excellent solutions."
"One of the valuable features of Dataiku is the workflow capability."
"The advantage is that you can focus on machine learning while having access to what they call 'recipes.' These recipes allow me to preprocess and prepare data without writing any code."
"Our clients can easily drag and drop components and use them on the spot."
"Dataiku is really a very intuitive platform that allows you to carry out data projects from end to end, with the opportunity to reuse templates, models, and recipes, which is one of the big advantages of using it."
"The solution is quite stable."
"I consider the return on investment with Dataiku valuable because for us, it is one single platform where all our data scientists come together and work on any model building, so it is collaboration, plus having everything in one place, organized, having proper project management, and then built-in capabilities which help to facilitate model building."
"Extremely easy to use with its GUI-based functionality and large compatibility with various data sources. Also, maintenance processes are much more automated than ever, with fewer errors."
"The best features Dataiku offers include the ability for users to use the node without having to code and the functionality related to low-code/no-code."
 

Cons

"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."
"It is a bit complex to scale. It is still evolving as a product."
"All products have room for improvement, and I would like to see their pricing simplified, as it is somewhat complex."
"I think it would help if Data Science Studio added some more features and improved the data model."
"The license is very expensive. It would be great to have an intermediate license for basic treatments that do not require extensive experience."
"The license is very expensive."
"In the next release of this solution, I would like to see deep learning better integrated into the tool and not simply an extension or plugin."
"We still encounter some integration issues."
"I find that it is a little slow during use. It takes more time than I would expect for operations to complete."
"I think the pricing and licensing of Dataiku is a bit expensive; it could be improved further, and I think they should have a different kind of licensing model as well."
 

Pricing and Cost Advice

Information not available
"Pricing is pretty steep. Dataiku is also not that cheap."
"The annual licensing fees are approximately €20 ($22 USD) per key for the basic version and €40 ($44 USD) per key for the version with everything."
report
Use our free recommendation engine to learn which Data Science Platforms solutions are best for your needs.
884,873 professionals have used our research since 2012.
 

Top Industries

By visitors reading reviews
No data available
Financial Services Firm
18%
Manufacturing Company
10%
Computer Software Company
9%
Energy/Utilities Company
6%
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
No data available
By reviewers
Company SizeCount
Small Business6
Midsize Enterprise2
Large Enterprise13
 

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 ...
What is your experience regarding pricing and costs for Dataiku Data Science Studio?
I am not the person involved in the process regarding pricing, setup cost, and licensing.
What needs improvement with Dataiku Data Science Studio?
To improve Dataiku, it could enhance its visualization features, as it is not possible in Dataiku to create direct visualizations or to integrate a web app directly or in a simpler way as it is pos...
What is your primary use case for Dataiku Data Science Studio?
My main use case for Dataiku is for data science and AI projects. I use Dataiku for a demand forecasting use case where the objective is to predict the demand for each product for the next four mon...
 

Comparisons

 

Also Known As

No data available
Dataiku DSS
 

Overview

 

Sample Customers

LexisNexis, Vibes, FINRA, VidMob
BGL BNP Paribas, Dentsu Aegis, Link Mobility Group, AramisAuto
Find out what your peers are saying about Amazon Comprehend vs. Dataiku and other solutions. Updated: March 2026.
884,873 professionals have used our research since 2012.