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Amazon SageMaker vs Azure OpenAI comparison

 

Comparison Buyer's Guide

Executive SummaryUpdated on Apr 20, 2025

Review summaries and opinions

We asked business professionals to review the solutions they use. Here are some excerpts of what they said:
 

ROI

Sentiment score
7.3
Amazon SageMaker offers significant ROI with cost reductions, time savings, and notable financial benefits, especially in fraud detection and targeted ads.
Sentiment score
7.6
Azure OpenAI boosts productivity, reduces costs, accelerates project delivery, and improves efficiency without significant infrastructure investment.
The return on investment varies by use case and offers significant value in revenue increases and cost saving capabilities, especially in real time fraud detection and targeted advertisements.
 

Customer Service

Sentiment score
7.2
Amazon SageMaker customer service has mixed reviews, with satisfaction varying based on user experience, support promptness, and service tier.
Sentiment score
5.9
Azure OpenAI's customer service is helpful but inconsistent, with slow responses and challenges accessing expertise during high demand.
The technical support from AWS is excellent.
The support is very good with well-trained engineers.
Tickets can be prioritized for critical issues.
It is important for organizations like Microsoft to apply OpenAI solutions within their own structures.
 

Scalability Issues

Sentiment score
7.6
Amazon SageMaker is highly scalable, handling diverse data needs effectively but may require deployment expertise for optimal efficiency.
Sentiment score
6.7
Azure OpenAI's scaling is seen as adaptable yet challenged by capacity, costs, and configuration, with improvements noted in newer releases.
The availability of GPU instances can be a challenge, requiring proper planning.
Amazon SageMaker is scalable and works well from an infrastructure perspective.
The API works fine, allowing me to scale indefinitely.
The scalability depends on whether the application is multimodal or uses a single model.
 

Stability Issues

Sentiment score
7.8
Amazon SageMaker is stable and reliable, with minor issues mainly due to user configuration errors, not infrastructure problems.
Sentiment score
7.6
Azure OpenAI generally receives high stability ratings, despite occasional performance issues, with room for improvement in integration.
I rate the stability of Amazon SageMaker between seven and eight.
The solution works fine, particularly for enterprises or even some small enterprises.
 

Room For Improvement

Amazon SageMaker needs UI simplification, better documentation, cost efficiency improvements, enhanced security, scalability, training resources, and performance optimization.
Azure OpenAI needs updates in data management, security, user support, and regional availability to improve adoption and efficiency.
Integration of the latest machine learning models like the new Amazon LLM models could enhance its capabilities.
Having all documentation easily accessible on the front page of SageMaker would be a great improvement.
This would empower citizen data scientists to utilize the tool more effectively since many data scientists do not have a core development background.
Expanding token limitations for scaling while ensuring concurrent user access is crucial.
Azure needs to work on its own model development and improve the integration of voice-to-text services.
 

Setup Cost

Amazon SageMaker is costly, especially for notebook instances, with better visibility needed to optimize pay-as-you-go costs.
Azure OpenAI pricing is flexible but costly; enterprise agreements and financial controls can help manage expenses effectively.
For a single user, prices might be high yet could be cheaper for user-managed services compared to AWS-managed services.
The cost for small to medium instances is not very high.
The pricing can be up to eight or nine out of ten, making it more expensive than some cloud alternatives yet more economical than on-premises setups.
Recent iterations have increased token allowances, mitigating some challenges associated with concurrent user access at scale.
The pricing is very good for handling various kinds of jobs.
 

Valuable Features

Amazon SageMaker provides flexible AI/ML solutions with easy AWS integration, strong deployment features, and comprehensive tools for scalability.
Azure OpenAI offers advanced features like GPT-3.5, integration, scalability, security, and ease, enhancing operations with predictive analytics.
This allows monitoring and performance grading, as I instantly know when someone has a bad call.
The most valuable features include the ML operations that allow for designing, deploying, testing, and evaluating models.
SageMaker supports building, training, and deploying AI models from scratch, which is crucial for my ML project.
OpenAI models help me create predictive analysis products and chat applications, enabling me to automate tasks and reduce the workforce needed for repetitive work, thereby streamlining operations.
 

Categories and Ranking

Amazon SageMaker
Ranking in AI Development Platforms
5th
Average Rating
7.8
Reviews Sentiment
7.1
Number of Reviews
36
Ranking in other categories
Data Science Platforms (3rd)
Azure OpenAI
Ranking in AI Development Platforms
1st
Average Rating
7.8
Reviews Sentiment
6.7
Number of Reviews
33
Ranking in other categories
No ranking in other categories
 

Mindshare comparison

As of April 2025, in the AI Development Platforms category, the mindshare of Amazon SageMaker is 5.6%, down from 8.6% compared to the previous year. The mindshare of Azure OpenAI is 12.9%, down from 21.1% compared to the previous year. It is calculated based on PeerSpot user engagement data.
AI Development Platforms
 

Featured Reviews

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.
Viswanath Barenkala - PeerSpot reviewer
Offers tools to moderate generated content and guidance to safely design applications, but it is not consistently accessible
Instead of a feature, the GPT-4 model has been most beneficial for automating tasks. We transitioned from GPT-3.5 to GPT-4 and actively use it. However, we face limitations due to geographic availability, subscription constraints, and rate limiting, which we are currently negotiating and working towards optimizing. While we haven't formally benchmarked Azure OpenAI's language understanding against industry standards, we find it performs well about 70-80% of the time. Occasionally, we need to refine our queries and adapt our systems accordingly to improve accuracy and effectiveness.
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Top Industries

By visitors reading reviews
Financial Services Firm
19%
Educational Organization
11%
Computer Software Company
11%
Manufacturing Company
9%
Financial Services Firm
15%
Computer Software Company
13%
Manufacturing Company
11%
Educational Organization
6%
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
 

Questions from the Community

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.
What do you like most about Azure OpenAI?
The product is easy to integrate with our IT workflow.
What is your experience regarding pricing and costs for Azure OpenAI?
In the past, the primary expense involved token limitations which constrained scaling. Recent iterations have increased token allowances, mitigating some challenges associated with concurrent user ...
What needs improvement with Azure OpenAI?
Azure ( /products/microsoft-azure-reviews ) could significantly benefit from including more LLM models apart from OpenAI, as I often need to switch clouds when a model doesn't meet my requirements....
 

Also Known As

AWS SageMaker, SageMaker
No data available
 

Overview

 

Sample Customers

DigitalGlobe, Thomson Reuters Center for AI and Cognitive Computing, Hotels.com, GE Healthcare, Tinder, Intuit
Information Not Available
Find out what your peers are saying about Amazon SageMaker vs. Azure OpenAI and other solutions. Updated: April 2025.
848,716 professionals have used our research since 2012.