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Compare AWS Bedrock and Azure Machine Learning 
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At a Glance
Market Segments
Enterprise (38.6% of reviews)
Enterprise (38.8% of reviews)
Entry-Level Pricing
No pricing available
No pricing available
AWS Bedrock
Star Rating
(44)4.4 out of 5
Market Segments
Enterprise (38.6% 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 LearningSummary
AI Generated Summary
AI-generated. Powered by real user reviews.
- Users report that Azure Machine Learning excels in Ease of Use with a score of 8.6, while AWS Bedrock has a slightly lower score of 8.3. Reviewers mention that Azure's intuitive interface and comprehensive documentation make it easier for beginners to get started.
- Reviewers mention that AWS Bedrock shines in Scalability, achieving a score of 9.2 compared to Azure's 8.9. Users on G2 highlight AWS's robust infrastructure that supports large-scale deployments and high availability for generative AI applications.
- Users say that Azure Machine Learning offers superior Model Development features, particularly in Drag and Drop functionality, scoring 8.9 versus AWS Bedrock's 8.2. Reviewers appreciate Azure's user-friendly tools that simplify the model training process.
- G2 users report that AWS Bedrock provides better Natural Language Processing capabilities, with a score of 8.7 compared to Azure's 7.9. Reviewers mention that AWS's pre-built algorithms for NLP tasks are more effective and easier to implement.
- Users on G2 highlight Azure's strong Quality of Support, both scoring 8.6, but reviewers mention that Azure's support team is more responsive and helpful in resolving issues quickly, enhancing the overall user experience.
- Reviewers mention that Azure Machine Learning has a more comprehensive AI GDPR and Regulatory Compliance feature, scoring 7.9, while AWS Bedrock's score is lower at 7.6. Users appreciate Azure's proactive approach to compliance, which is crucial for enterprise-level applications.
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
8.6
44
8.5
81
Ease of Use
8.1
44
8.5
80
Ease of Setup
8.4
43
8.3
57
Ease of Admin
9.0
15
8.3
49
Quality of Support
8.5
43
8.6
74
Has the product been a good partner in doing business?
8.5
14
8.6
47
Product Direction (% positive)
9.5
43
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
8.9
14
Not enough data
8.5
13
Not enough data
8.2
14
Not enough data
Cost and Efficiency - Generative AI Infrastructure
8.1
14
Not enough data
8.5
14
Not enough data
7.9
14
Not enough data
Integration and Extensibility - Generative AI Infrastructure
8.6
14
Not enough data
8.8
14
Not enough data
8.2
14
Not enough data
Security and Compliance - Generative AI Infrastructure
8.6
14
Not enough data
8.7
14
Not enough data
8.5
14
Not enough data
Usability and Support - Generative AI Infrastructure
7.7
14
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
22.7%
Computer Software
18.2%
Financial Services
6.8%
Consulting
4.5%
Retail
4.5%
Other
43.2%
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
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