Amazon Comprehend vs Dataiku comparison

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Amazon Comprehend

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Executive Summary

We performed a comparison between Amazon Comprehend and Dataiku based on real PeerSpot user reviews.

Find out in this report how the two Data Science Platforms solutions compare in terms of features, pricing, service and support, easy of deployment, and ROI.

To learn more, read our detailed Amazon Comprehend vs. Dataiku Report (Updated: June 2026).

Amazon Comprehend vs. Dataiku

June 2026

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Helped 900,644 peers since 2012

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

Ranking in other categories

No ranking in other categories

Dataiku

Ranking in Data Science Platforms

2nd

Average Rating

8.2

Reviews Sentiment

6.5

Number of Reviews

Ranking in other categories

No ranking in other categories

Mindshare comparison

As of June 2026, in the Data Science Platforms category, the mindshare of Amazon Comprehend is 1.0%, up from 0.5% compared to the previous year. The mindshare of Dataiku is 5.2%, down from 13.0% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Data Science Platforms Mindshare Distribution
Product	Mindshare (%)
Dataiku	5.2%
Amazon Comprehend	1.0%
Other	93.8%

Data Science Platforms

Featured Reviews

Ashish Lata

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.

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Shubhamkumar Gomar

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.

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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

"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."

"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."

"The solution is quite stable."

"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."

"One of the valuable features of Dataiku is the workflow capability."

"Cloud-based process run helps in not keeping the systems on while processes are running."

"Technical support is really, really good."

"Using Dataiku has meant that we spend less time on preparing and cleaning data, and we spend less time on blending models together, ultimately meaning that we can spend more time modeling."

"I rate the overall product as eight out of ten."

More Dataiku pros

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."

"Although known for Big Data, the processing time to process 1.8 billion records was terribly slow (five days)."

"I have not seen a return on investment with Dataiku in terms of time saved, money saved, or fewer employees needed."

"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."

"The interface for the web app can be a bit difficult. It needs to have better capabilities, at least for developers who like to code."

"The interface for the web app can be a bit difficult. It needs to have better capabilities, at least for developers who like to code. This is due to the fact that everything is enabled in a single window with different tabs. For them to actually develop and do the concurrent testing that needs to be done, it takes a bit of time. That is one improvement that I would like to see - from a web app developer perspective."

"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."

"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."

More Dataiku cons

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

19%

Manufacturing Company

10%

Computer Software Company

Energy/Utilities Company

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

No data available

By reviewers
Company Size	Count
Small Business	6
Midsize Enterprise	2
Large Enterprise	13

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 licenses are a bit high for companies that are still hesitating to get started with using Dataiku. For my personal projects, I used the thirty-day free trial. Regarding my company, I did not ha...

What needs improvement with Dataiku Data Science Studio?

I have no suggestions for improvements because it's all good; it just sometimes lags a lot, and I don't know if the server is full or what, but it sometimes takes a lot of time while loading and re...

What is your primary use case for Dataiku Data Science Studio?

My main use case for Dataiku involves ETL pipelines, mainly for data analysis, and I majorly use SQL queries for that. For ETL pipelines and data analysis, I had to create the output by combining a...

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Product Reports

Download Amazon Comprehend product report

Data Science Platforms

May 2026

Download Dataiku product report

June 2026

Also Known As

No data available

Dataiku DSS

Overview

Amazon Comprehend is a powerful tool that enables businesses to effectively analyze text data and extract useful insights. It accelerates data-driven decisions by applying Natural Language Processing to a wide range of business contexts.

Focusing on advanced Natural Language Processing, Amazon Comprehend allows enterprises to uncover hidden patterns and relationships in textual data. It supports name entity recognition, sentiment analysis, keyphrase extraction, language detection, and more. Businesses can leverage these capabilities to gain meaningful insights from customer feedback, documents, and other unstructured data sources, converting information into actionable intelligence efficiently.

What are the key features of Amazon Comprehend?

Entity Recognition: Identifies people, places, and other entities within text.
Sentiment Analysis: Determines sentiment expressed in text, such as positive or negative emotions.
Language Detection: Identifies primary language used in text data.
Keyphrase Extraction: Extracts significant phrases and terms from text.
Topic Modeling: Groups similar data points for better categorization and understanding.

What benefits and ROI should users look for?

Improved Decision Making: Facilitates data-informed strategies through comprehensive text analysis.
Enhanced Customer Understanding: Offers deep insights into customer feedback and sentiment.
Operational Efficiency: Streamlines text processing, saving time and resources.
Scalability: Adapts to changing business needs seamlessly, handling large volumes of data.

In healthcare, Amazon Comprehend is implemented to analyze patient sentiments and feedback, leading to improved care. In finance, it assists in sentiment analysis for market research, aiding strategic decision making. Retailers use it to interpret customer opinions and enhance service offerings.

Amazon Web Services (AWS)

Dataiku Data Science Studio is acclaimed for its versatile capabilities in advanced analytics, data preparation, machine learning, and visualization. It streamlines complex data tasks with an intuitive visual interface, supports multiple languages like Python, R, SQL, and scales efficiently for large dataset handling, boosting organizational efficiency and collaboration.

Sample Customers

LexisNexis, Vibes, FINRA, VidMob

BGL BNP Paribas, Dentsu Aegis, Link Mobility Group, AramisAuto