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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
7.1
Number of Reviews
37
Ranking in other categories
AI Development Platforms (5th)
 

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 Amazon SageMaker is 7.3%, down from 9.8% 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.
Hemant Paralkar - PeerSpot reviewer
Improves team collaboration with advanced feature sharing but needs a better user experience
Improvement is needed in the no-code and low-code capabilities of Amazon SageMaker. This would empower citizen data scientists to utilize the tool more effectively since many data scientists do not have a core development background. Additionally, dealing with frequent UI updates can be challenging, especially for infrastructure architects like myself. It involves effort to migrate to new UIs, making the updates not seamless. User auditing requires enhancements as tracking operations performed by users can be difficult due to dynamic IP validation and role propagation.

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."
"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 these models, making accessing them convenient as needed."
"We were able to use the product to automate processes."
"Amazon SageMaker is highly valuable for managing ML workloads. It connects to AWS cloud resources, making it easy to deploy algorithms and collaborate using tools like GitLab. It offers a wide range of Python libraries and other necessary tools for modelling and algorithms."
"The most valuable features in Amazon SageMaker are its AutoML, feature store, and automated hyperparameter tuning capabilities."
"The tool has made client management easier where patients need to upload their health records and we can use the tool to understand details on treatment date, amount, etc."
"We've had no problems with SageMaker's stability."
"The solution's ability to improve work at my organization stems from the ensemble model and a combination of various models it provides."
"The tool makes our ML model development a bit more efficient because everything is in one environment."
 

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."
"The solution is complex to use."
"AI is a new area and AWS needs to have an internship training program available."
"SageMaker would be improved with the addition of reporting services."
"The model repository is a concern as models are stored on a bucket and there's an issue with versioning."
"There are other better solutions for large data, such as Databricks."
"There is room for improvement in the collaboration with serverless architecture, particularly integration with AWS Lambda."
"The main challenge with Amazon SageMaker is the integrations."
"For any cloud provider, the cost has to be substantially reduced, especially in the case of Amazon SageMaker, which is extremely expensive for huge workloads."
 

Pricing and Cost Advice

Information not available
"The pricing is complicated as it is based on what kind of machines you are using, the type of storage, and the kind of computation."
"The cost offers a pay-as-you-go pricing model. It depends on the instance that you do."
"There is no license required for the solution since you can use it on demand."
"In terms of pricing, I'd also rate it ten out of ten because it's been beneficial compared to other solutions."
"SageMaker is worth the money for our use case."
"You don't pay for Sagemaker. You only pay for the compute instances in your storage."
"I would rate the solution's price a ten out of ten since it is very high."
"The solution is relatively cheaper."
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Top Industries

By visitors reading reviews
No data available
Financial Services Firm
19%
Educational Organization
11%
Computer Software Company
11%
Manufacturing 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 ...
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?
Before deploying SageMaker, I reviewed the pricing, especially for notebook instances. The cost for small to medium instances is not very high.
 

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: April 2025.
849,686 professionals have used our research since 2012.