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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
19th
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
6th
Average Rating
8.2
Reviews Sentiment
7.1
Number of Reviews
12
Ranking in other categories
No ranking in other categories
 

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 Dataiku is 12.7%, up from 8.2% 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.
RichardXu - PeerSpot reviewer
The platform organizes workflows visually and efficiently
One of the valuable features of Dataiku is the workflow capability. It allows us to organize a workflow efficiently. The platform has a visual interface, making it much easier for educated professionals to organize their work. This feature is useful because it simplifies tasks and eliminates the need for a data scientist. If you are knowledgeable about AI, you can directly write using primitive tools like Pantera flow, PyTorch, and Scikit-learn. However, Dataiku makes this process much easier.

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."
"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."
"If many teams are collaborating and sharing Jupyter notebooks, it's very useful."
"The most valuable feature is the set of visual data preparation tools."
"Our clients can easily drag and drop components and use them on the spot."
"The most valuable feature of this solution is that it is one tool that can do everything, and you have the ability to very easily push your design to prediction."
"Data Science Studio's data science model is very useful."
"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."
"Cloud-based process run helps in not keeping the systems on while processes are running."
 

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."
"Server up-time needs to be improved. Also, query engines like Spark and Hive need to be more stable."
"There were stability issues: 1) SQL operations, such as partitioning, had bugs and showed wrong results. 2) Due to server downtime, scheduled processes used to fail. 3) Access to project folders was compromised (privacy issue) with wrong people getting access to confidential project folders."
"One of the main challenges was collaboration. Developers typically use GitHub to push and manage code, but integrating GitHub with Dataiku was complicated."
"The ability to have charts right from the explorer would be an improvement."
"The technical support from Dataiku is not good. The support team does not provide adequate assistance, and there are concerns about billing requests."
"Dataiku still needs some coding, and that could be a difference where business data scientists would go for DataRobot more than Dataiku."
"There is room for improvement in terms of allowing for more code-based features."
"I find that it is a little slow during use. It takes more time than I would expect for operations to complete."
 

Pricing and Cost Advice

Information not available
"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."
"Pricing is pretty steep. Dataiku is also not that cheap."
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Top Industries

By visitors reading reviews
No data available
Financial Services Firm
18%
Educational Organization
13%
Manufacturing Company
9%
Computer Software Company
9%
 

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 ...
What is your experience regarding pricing and costs for Dataiku Data Science Studio?
The pricing for Dataiku is very high, which is its biggest downside. The model they follow is not consumption-based, making it expensive.
What needs improvement with Dataiku Data Science Studio?
Dataiku's pricing is very high, and commercial transparency is a challenge. Support is also an area needing improvement. More features like LLM security, holographic encryption, and enhanced GPU in...
What is your primary use case for Dataiku Data Science Studio?
My primary use case for Dataiku ( /products/dataiku-reviews ) is for data science, Gen ( /products/gen-reviews ) AI, and data science applications. Our AGN team also uses it for various purposes.
 

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 2025.
846,617 professionals have used our research since 2012.