Introducing G2.ai, the future of software buying.Try now
Compare AWS Bedrock and Azure Machine Learning 
Featured Products
At a Glance
Market Segments
Enterprise (40.4% of reviews)
Enterprise (38.8% of reviews)
Entry-Level Pricing
No pricing available
No pricing available
AWS Bedrock
Star Rating
(47)4.3 out of 5
Market Segments
Enterprise (40.4% of reviews)
Pros & Cons
Entry-Level Pricing
No pricing available
Learn more about AWS BedrockAzure Machine Learning
Star Rating
(88)4.3 out of 5
Market Segments
Enterprise (38.8% of reviews)
Pros & Cons
Entry-Level Pricing
No pricing available
Learn more about Azure Machine LearningAWS Bedrock vs Azure Machine Learning
When assessing the two solutions, reviewers found Azure Machine Learning easier to use and do business with overall. However, reviewers preferred the ease of set up with AWS Bedrock, along with administration.
- Reviewers felt that AWS Bedrock meets the needs of their business better than Azure Machine Learning.
- When comparing quality of ongoing product support, reviewers felt that Azure Machine Learning is the preferred option.
- For feature updates and roadmaps, our reviewers preferred the direction of AWS Bedrock over Azure Machine Learning.
Pricing
Entry-Level Pricing
AWS Bedrock
No pricing availableAzure Machine Learning
No pricing availableFree Trial
AWS Bedrock
No trial information availableAzure Machine Learning
No trial information availableRatings
Meets Requirements
8.7
47
8.5
81
Ease of Use
8.0
47
8.5
80
Ease of Setup
8.4
46
8.3
57
Ease of Admin
9.0
17
8.3
49
Quality of Support
8.4
46
8.6
74
Has the product been a good partner in doing business?
8.5
16
8.6
47
Product Direction (% positive)
9.2
46
9.0
80
Features by Category
Deployment
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Deployment
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Management
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Operations
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Management
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Generative AI
Not enough data
Not enough data
Not enough data
Not enough data
System
Not enough data
8.6
22
Model Development
Not enough data
8.6
51
Not enough data
8.9
54
Not enough data
8.3
53
Not enough data
8.7
52
Model Development
Not enough data
8.4
21
Machine/Deep Learning Services
Not enough data
8.1
45
Not enough data
7.9
45
Not enough data
7.8
38
Not enough data
8.2
42
Machine/Deep Learning Services
Not enough data
8.7
21
Not enough data
8.5
21
Deployment
Not enough data
8.8
50
Not enough data
8.7
51
Not enough data
8.9
51
Generative AI
Not enough data
8.5
10
Not enough data
8.2
10
Not enough data
7.5
10
Agentic AI - Data Science and Machine Learning Platforms
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Scalability and Performance - Generative AI Infrastructure
9.1
16
Not enough data
8.6
14
Not enough data
8.0
15
Not enough data
Cost and Efficiency - Generative AI Infrastructure
8.0
16
Not enough data
8.6
16
Not enough data
7.9
14
Not enough data
Integration and Extensibility - Generative AI Infrastructure
8.6
14
Not enough data
9.0
16
Not enough data
8.4
16
Not enough data
Security and Compliance - Generative AI Infrastructure
8.6
14
Not enough data
8.8
15
Not enough data
8.5
14
Not enough data
Usability and Support - Generative AI Infrastructure
7.4
16
Not enough data
7.7
14
Not enough data
Prompt Engineering - Large Language Model Operationalization (LLMOps)
8.0
5
Not enough data
7.3
5
Not enough data
Inference Optimization - Large Language Model Operationalization (LLMOps)
Not enough data
Not enough data
Model Garden - Large Language Model Operationalization (LLMOps)
8.7
5
Not enough data
Custom Training - Large Language Model Operationalization (LLMOps)
7.7
5
Not enough data
Application Development - Large Language Model Operationalization (LLMOps)
Not enough data
Not enough data
Model Deployment - Large Language Model Operationalization (LLMOps)
Not enough data
Not enough data
8.3
5
Not enough data
Guardrails - Large Language Model Operationalization (LLMOps)
Not enough data
Not enough data
Not enough data
Not enough data
Model Monitoring - Large Language Model Operationalization (LLMOps)
Not enough data
Not enough data
Not enough data
Not enough data
Security - Large Language Model Operationalization (LLMOps)
9.0
5
Not enough data
Not enough data
Not enough data
Gateways & Routers - Large Language Model Operationalization (LLMOps)
Not enough data
Not enough data
Customization - AI Agent Builders
7.9
7
Not enough data
7.6
7
Not enough data
7.7
8
Not enough data
Functionality - AI Agent Builders
7.5
8
Not enough data
7.4
7
Not enough data
7.4
7
Not enough data
8.1
7
Not enough data
Data and Analytics - AI Agent Builders
8.1
7
Not enough data
8.1
7
Not enough data
9.3
7
Not enough data
Integration - AI Agent Builders
8.3
7
Not enough data
8.3
7
Not enough data
7.9
7
Not enough data
7.5
6
Not enough data
Data Ingestion & Preparation - Low-Code Machine Learning Platforms
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Model Construction & Automation - Low-Code Machine Learning Platforms
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Categories
Categories
Shared Categories
AWS Bedrock and Azure Machine Learning are categorized as Large Language Model Operationalization (LLMOps) and Generative AI Infrastructure
Unique Categories
AWS Bedrock is categorized as AI Agent Builders
Azure Machine Learning is categorized as MLOps Platforms, Data Science and Machine Learning Platforms, and Low-Code Machine Learning Platforms
Reviews
Reviewers' Company Size
AWS Bedrock
Small-Business(50 or fewer emp.)
Mid-Market(51-1000 emp.)
Enterprise(> 1000 emp.)
Azure Machine Learning
Small-Business(50 or fewer emp.)
Mid-Market(51-1000 emp.)
Enterprise(> 1000 emp.)
Small-Business
(50 or fewer emp.)
Mid-Market
(51-1000 emp.)
Enterprise
(> 1000 emp.)
Reviewers' Industry
Information Technology and Services
21.3%
Computer Software
19.1%
Financial Services
8.5%
Consulting
6.4%
Retail
4.3%
Other
40.4%
Information Technology and Services
28.2%
Computer Software
14.1%
Management Consulting
8.2%
Education Management
5.9%
Higher Education
4.7%
Other
38.8%
Alternatives
Discussions
AWS Bedrock has no discussions with answers
What is Azure Machine Learning Studio used for?
1 Comment
AR
In short, to build, deploy, and manage high-quality models faster and with confidence.Read more
Azure Machine Learning has no more discussions with answers
Hunting for software insights?
With over 3 million reviews, we can provide the specific details that help you make an informed software buying decision for your business. Finding the right product is important, let us help.
