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Amazon Comprehend vs Amazon SageMaker 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
Amazon SageMaker
Ranking in Data Science Platforms
3rd
Average Rating
7.8
Reviews Sentiment
7.0
Number of Reviews
38
Ranking in other categories
AI Development Platforms (5th)
 

Mindshare comparison

As of August 2025, in the Data Science Platforms category, the mindshare of Amazon Comprehend is 0.5%, down from 0.6% compared to the previous year. The mindshare of Amazon SageMaker is 6.1%, down from 8.8% 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.
Saurabh Jaiswal - PeerSpot reviewer
Create innovative assistants with seamless data integration for large-scale projects
The various integration options available in Amazon SageMaker ( /products/amazon-sagemaker-reviews ), such as Firehose for connecting to data pipelines, are simple to use. Tools like AWS Glue ( /products/aws-glue-reviews ) integrate well for data transformations. The Databricks ( /products/databricks-reviews ) integration aids data scientists and engineers. SageMaker is fully managed, offers high availability, flexibility with TensorFlow ( /products/tensorflow-reviews ), PyTorch ( /products/pytorch-reviews ), and MXNet ( /products/mxnet-reviews ), and comes with pre-trained algorithms for forecasting, anomaly detection, and more.

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."
"SageMaker supports building, training, and deploying AI models from scratch, which is crucial for my ML project."
"They offer insights into everyone making calls in my organization."
"The support is very good with well-trained engineers whose training curriculum is rigorous."
"The most valuable features are the ability to store artifacts and gather reports and measures from experiments."
"The technical support of the tool was good."
"I appreciate the ease of use in Amazon SageMaker."
"It's user-friendly for business teams as they can understand many aspects through the AWS interface."
"SageMaker offers functionalities like Jupyter Notebooks for development, built-in algorithms, model tuning, and options to deploy models on managed infrastructure."
 

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."
"In my opinion, one improvement for Amazon SageMaker would be to offer serverless GPUs. Currently, we incur costs on an hourly basis. It would be beneficial if the tool could provide pay-as-you-go pricing based on endpoints."
"Scalability to handle big data can be improved by making integration with networks such as Hadoop and Apache Spark easier."
"Amazon SageMaker could improve in the area of hyperparameter tuning by offering more automated suggestions and tips during the tuning process."
"The user interface (UI) and user experience (UX) of SageMaker and AWS, in general, need improvement as they are not intuitive and require substantial time to learn how to use specific services."
"The documentation must be made clearer and more user-friendly."
"There is room for improvement in the collaboration with serverless architecture, particularly integration with AWS Lambda."
"One area for improvement is the pricing, which can be quite high."
"Creating notebook instances for multiple users is pretty expensive in Amazon SageMaker."
 

Pricing and Cost Advice

Information not available
"Amazon SageMaker is a very expensive product."
"The tool's pricing is reasonable."
"I would rate the solution's price a ten out of ten since it is very high."
"The solution is relatively cheaper."
"I rate the pricing a five on a scale of one to ten, where one is the lowest price, and ten is the highest price. The solution is priced reasonably. There is no additional cost to be paid in excess of the standard licensing fees."
"The cost offers a pay-as-you-go pricing model. It depends on the instance that you do."
"SageMaker is worth the money for our use case."
"The pricing could be better, especially for querying. The per-query model feels expensive."
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Top Industries

By visitors reading reviews
No data available
Financial Services Firm
19%
Computer Software Company
11%
Manufacturing Company
9%
University
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 ...
How would you compare Databricks vs Amazon SageMaker?
We researched AWS SageMaker, but in the end, we chose Databricks. Databricks is a Unified Analytics Platform designed to accelerate innovation projects. It is based on Spark so it is very fast. It...
What do you like most about Amazon SageMaker?
We've had experience with unique ML projects using SageMaker. For example, we're developing a platform similar to ChatGPT that requires models. We utilize Amazon SageMaker to create endpoints for t...
What is your experience regarding pricing and costs for Amazon SageMaker?
The pricing is high, around an eight. However, SageMaker offers free trials for the first two months, allowing users to determine which features they need. It is considered value for money given it...
 

Also Known As

No data available
AWS SageMaker, SageMaker
 

Overview

 

Sample Customers

LexisNexis, Vibes, FINRA, VidMob
DigitalGlobe, Thomson Reuters Center for AI and Cognitive Computing, Hotels.com, GE Healthcare, Tinder, Intuit
Find out what your peers are saying about Amazon Comprehend vs. Amazon SageMaker and other solutions. Updated: July 2025.
864,155 professionals have used our research since 2012.