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Amazon Comprehend vs H2O.ai 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
H2O.ai
Ranking in Data Science Platforms
20th
Average Rating
7.6
Reviews Sentiment
7.2
Number of Reviews
8
Ranking in other categories
Model Monitoring (6th)
 

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 H2O.ai is 1.5%, up from 1.5% 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.
Kashif Yaseen - PeerSpot reviewer
Plug-and-play convenience enhances productivity but needs better multimodal support
We mostly used the solution in the domain that I'm working. We had most of the use cases around chatbots and conversational BI The solution was plug-and-play, meaning most of the components were handled by the solution itself rather than building them from scratch. This was useful for our banking…

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 most valuable feature of H2O.ai is that it is plug-and-play."
"The ease of use in connecting to our cluster machines."
"It is helpful, intuitive, and easy to use. The learning curve is not too steep."
"One of the most interesting features of the product is their driverless component. The driverless component allows you to test several different algorithms along with navigating you through choosing the best algorithm."
"Fast training, memory-efficient DataFrame manipulation, well-documented, easy-to-use algorithms, ability to integrate with enterprise Java apps (through POJO/MOJO) are the main reasons why we switched from Spark to H2O."
"The most valuable features are the machine learning tools, the support for Jupyter Notebooks, and the collaboration that allows you to share it across people."
"AutoML helps in hands-free initial evaluations of efficiency/accuracy of ML algorithms."
 

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."
"On the topic of model training and model governance, this solution cannot handle ten or twelve models running at the same time."
"I would like to see more features related to deployment."
"It needs a drag and drop GUI like KNIME, for easy access to and visibility of workflows."
"It lacks the data manipulation capabilities of R and Pandas DataFrames. We would kill for dplyr offloading H2O."
"The model management features could be improved."
"H2O.ai can improve in areas like multimodal support and prompt engineering."
"Referring to bullet-3 as well, H2O DataFrame manipulation capabilities are too primitive."
"The interpretability module has room for improvement. Also, it needs to improve its ability to integrate with other systems, like SageMaker, and the overall integration capability."
 

Pricing and Cost Advice

Information not available
"We have seen significant ROI where we were able to use the product in certain key projects and could automate a lot of processes. We were even able to reduce staff."
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846,617 professionals have used our research since 2012.
 

Top Industries

By visitors reading reviews
No data available
Financial Services Firm
20%
Computer Software Company
12%
Manufacturing Company
8%
Educational Organization
6%
 

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 needs improvement with H2O.ai?
H2O.ai can improve in areas like multimodal support and prompt engineering. They are already working on updates and changes. Although I haven't explored all the new products they've added to their ...
What is your primary use case for H2O.ai?
We mostly used the solution in the domain that I'm working. We had most of the use cases around chatbots and conversational BI.
What advice do you have for others considering H2O.ai?
It is important to address data privacy concerns and ensure you're choosing the right vendor that meets your use case demands. Also, you may leave my name, Kashif, but please keep the company name ...
 

Overview

 

Sample Customers

LexisNexis, Vibes, FINRA, VidMob
poder.io, Stanley Black & Decker, G5, PWC, Comcast, Cisco
Find out what your peers are saying about Amazon Comprehend vs. H2O.ai and other solutions. Updated: March 2025.
846,617 professionals have used our research since 2012.