Try our new research platform with insights from 80,000+ expert users

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 October 2025, in the Data Science Platforms category, the mindshare of Amazon Comprehend is 0.5%, down from 0.5% compared to the previous year. The mindshare of Amazon SageMaker is 5.7%, down from 7.9% compared to the previous year. It is calculated based on PeerSpot user engagement data.
Data Science Platforms Market Share Distribution
ProductMarket Share (%)
Amazon SageMaker5.7%
Amazon Comprehend0.5%
Other93.8%
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, 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 intuitive interface and streamlined user experience make it easy to navigate and set up various tools like Visual Studio Code or Jupyter Notebook."
"The solution is easy to scale...The documentation and online community support have been sufficient for us so far."
"I have seen a return on investment, probably a factor of four or five."
"The technical support from AWS is excellent."
"I recommend SageMaker for ML projects if you need to build models from scratch."
"The various integration options available in Amazon SageMaker, such as Firehose for connecting to data pipelines, are simple to use."
"The superb thing that SageMaker brings is that it wraps everything well. It's got the deployment, the whole framework."
"The most valuable feature of Amazon SageMaker is its integration. For example, AWS Lambda. Additionally, we can write Python code."
 

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 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."
"Creating notebook instances for multiple users is pretty expensive in Amazon SageMaker."
"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."
"The main challenge with Amazon SageMaker is the integrations."
"The dashboard could be improved by including more features and providing more information about deployed models, their drift, performance, scaling, and customization options."
"The model repository is a concern as models are stored on a bucket and there's an issue with versioning."
"SageMaker would be improved with the addition of reporting services."
"When starting a new session, the waiting time can be quite long, ranging from two to five minutes."
 

Pricing and Cost Advice

Information not available
"The solution is relatively cheaper."
"On average, customers pay about $300,000 USD per month."
"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 tool's pricing is reasonable."
"SageMaker is worth the money for our use case."
"Amazon SageMaker is a very expensive product."
"The product is expensive."
"The pricing is comparable."
report
Use our free recommendation engine to learn which Data Science Platforms solutions are best for your needs.
872,778 professionals have used our research since 2012.
 

Top Industries

By visitors reading reviews
No data available
Financial Services Firm
18%
Computer Software Company
11%
Manufacturing Company
8%
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: September 2025.
872,778 professionals have used our research since 2012.