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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
20th
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
2nd
Average Rating
7.8
Reviews Sentiment
7.0
Number of Reviews
38
Ranking in other categories
AI Development Platforms (4th)
 

Mindshare comparison

As of December 2025, in the Data Science Platforms category, the mindshare of Amazon Comprehend is 0.6%, up from 0.5% compared to the previous year. The mindshare of Amazon SageMaker is 5.0%, down from 7.6% compared to the previous year. It is calculated based on PeerSpot user engagement data.
Data Science Platforms Market Share Distribution
ProductMarket Share (%)
Amazon SageMaker5.0%
Amazon Comprehend0.6%
Other94.4%
Data Science Platforms
 

Featured Reviews

Ashish Lata - PeerSpot reviewer
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.
Saurabh Jaiswal - PeerSpot reviewer
Python AWS & AI Expert at a tech consulting company
Create innovative assistants with seamless data integration for large-scale projects
The various integration options available in Amazon SageMaker, such as Firehose for connecting to data pipelines, are simple to use. Tools like AWS Glue integrate well for data transformations. The Databricks integration aids data scientists and engineers. SageMaker is fully managed, offers high availability, flexibility with TensorFlow, PyTorch, and MXNet, 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

"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."
"The solution is easy to scale...The documentation and online community support have been sufficient for us so far."
"One of the most valuable features of Amazon SageMaker for me is the one-touch deployment, which simplifies the process greatly."
"I recommend SageMaker for ML projects if you need to build models from scratch."
"The most tool's valuable feature, in my experience, is hyperparameter tuning. It allows us to test different parameters for the same model in parallel, which helps us quickly identify the configuration that yields the highest accuracy. This parallel computing capability saves us a lot of time."
"Feature Store, CodeCommit, versioning, model control, and CI/CD pipelines are the most valuable features in Amazon SageMaker."
"The various integration options available in Amazon SageMaker, such as Firehose for connecting to data pipelines, are simple to use."
"The most valuable feature of Amazon SageMaker for me is the model deployment service."
"The evolution from SageMaker Classic to SageMaker Studio, particularly the UI part of Studio, is commendable."
 

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."
"The entry point can be a bit difficult. Having all documentation easily accessible on the front page of SageMaker would be a great improvement."
"The payment and monitoring metrics are a bit confusing not only for Amazon SageMaker but also for the range of other products that fall under AWS, especially for a new user of the product."
"The main challenge with Amazon SageMaker is the integrations."
"I would say the IDE is quite immature, but it is still in its infancy, so I expect it to get better over time."
"Amazon SageMaker can make it simpler to manage the data flow from start to finish, such as by integrating data, usingthe machine, and deploying models. This process could be more user-friendly compared to other tools. I would also like to improve integration with Bedrock and the LLM connection for AWS."
"The pricing of the solution is an issue...In SageMaker, monitoring could be improved by supporting more data types other than JSON and CSV."
"The training modules could be enhanced. We had to take in-person training to fully understand SageMaker, and while the trainers were great, I think more comprehensive online modules would be helpful."
"Lacking in some machine learning pipelines."
 

Pricing and Cost Advice

Information not available
"SageMaker is worth the money for our use case."
"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 solution is relatively cheaper."
"The tool's pricing is reasonable."
"You don't pay for Sagemaker. You only pay for the compute instances in your storage."
"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."
"Amazon SageMaker is a very expensive product."
"The product is expensive."
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Top Industries

By visitors reading reviews
No data available
Financial Services Firm
18%
Computer Software Company
11%
Manufacturing Company
9%
University
6%
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
No data available
By reviewers
Company SizeCount
Small Business12
Midsize Enterprise11
Large Enterprise16
 

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?
If you manage it effectively, their pricing is reasonable. It's similar to anything in the cloud; if you don't manage it properly, it can be expensive, but if you do, it's fine.
 

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: December 2025.
879,259 professionals have used our research since 2012.