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At a Glance
Market Segments
Enterprise (38.8% of reviews)
Mid-Market (42.6% of reviews)
Entry-Level Pricing
No pricing available
Free
Azure 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 Learningneptune.ai
Star Rating
(54)4.6 out of 5
Market Segments
Mid-Market (42.6% of reviews)
Pros & Cons
Entry-Level Pricing
Free
Browse all 4 pricing plansSummary
AI Generated Summary
AI-generated. Powered by real user reviews.
- Users report that Azure Machine Learning excels in its Ease of Setup with a score of 8.4, while neptune.ai shines with a higher score of 9.4, indicating a more user-friendly onboarding process.
- Reviewers mention that neptune.ai offers superior Quality of Support with a score of 9.7 compared to Azure Machine Learning's 8.6, highlighting a more responsive and helpful support team.
- G2 users note that Azure Machine Learning has a robust Framework Flexibility score of 9.3, which allows for a wider range of integrations, while neptune.ai also performs well with a score of 9.1, but may not support as many frameworks.
- Users say that both platforms provide excellent Scalability, but neptune.ai edges out with a score of 9.2, suggesting it may handle larger workloads more efficiently than Azure Machine Learning's score of 8.8.
- Reviewers mention that Azure Machine Learning's Model Registry feature, scoring 8.4, is beneficial for managing models, while neptune.ai's score of 8.3 indicates it has similar capabilities but may lack some advanced features.
- Users on G2 highlight that neptune.ai's Monitoring capabilities score 9.1, which is on par with Azure Machine Learning, but they appreciate neptune.ai's more intuitive interface for tracking model performance.
Pricing
Entry-Level Pricing
Azure Machine Learning
No pricing availableneptune.ai
Free (Cloud SaaS)
Free
For teams starting out with experiment tracking
- Up to 3 users
- 1 workspace
- 1 project
Free Trial
Azure Machine Learning
No trial information availableneptune.ai
Free Trial is available
Ratings
Meets Requirements
8.5
81
8.4
48
Ease of Use
8.5
80
9.1
52
Ease of Setup
8.3
57
9.2
32
Ease of Admin
8.3
49
8.8
10
Quality of Support
8.6
74
9.6
45
Has the product been a good partner in doing business?
8.6
47
8.7
10
Product Direction (% positive)
9.0
80
9.5
48
Features by Category
Deployment
Not enough data
8.7
36
Not enough data
9.3
38
Not enough data
9.0
39
Not enough data
Feature Not Available
Not enough data
8.9
33
Deployment
Not enough data
8.5
34
Not enough data
9.1
34
Not enough data
9.0
35
Not enough data
Feature Not Available
Not enough data
8.8
34
Management
Not enough data
8.5
34
Not enough data
9.1
37
Not enough data
8.3
33
Not enough data
8.2
34
Operations
Not enough data
8.3
32
Not enough data
Feature Not Available
Not enough data
9.2
32
Management
Not enough data
8.2
29
Not enough data
9.0
30
Not enough data
8.5
29
Generative AI
Not enough data
Feature Not Available
Not enough data
Feature Not Available
System
8.6
22
Not enough data
Model Development
8.6
51
Not enough data
8.9
54
Not enough data
8.3
53
Not enough data
8.7
52
Not enough data
Model Development
8.4
21
Not enough data
Machine/Deep Learning Services
8.1
45
Not enough data
7.9
45
Not enough data
7.8
38
Not enough data
8.2
42
Not enough data
Machine/Deep Learning Services
8.7
21
Not enough data
8.5
21
Not enough data
Deployment
8.8
50
Not enough data
8.7
51
Not enough data
8.9
51
Not enough data
Generative AI
8.5
10
Not enough data
8.2
10
Not enough data
7.5
10
Not enough data
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
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Cost and Efficiency - Generative AI Infrastructure
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Integration and Extensibility - Generative AI Infrastructure
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Security and Compliance - Generative AI Infrastructure
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Usability and Support - Generative AI Infrastructure
Not enough data
Not enough data
Not enough data
Not enough data
Large Language Model Operationalization (LLMOps)Hide 15 FeaturesShow 15 Features
Not enough data
Not enough data
Prompt Engineering - Large Language Model Operationalization (LLMOps)
Not enough data
Not enough data
Not enough data
Not enough data
Inference Optimization - Large Language Model Operationalization (LLMOps)
Not enough data
Not enough data
Model Garden - Large Language Model Operationalization (LLMOps)
Not enough data
Not enough data
Custom Training - Large Language Model Operationalization (LLMOps)
Not enough data
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
Not enough data
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)
Not enough data
Not enough data
Not enough data
Not enough data
Gateways & Routers - Large Language Model Operationalization (LLMOps)
Not enough data
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
Azure Machine Learning and neptune.ai are categorized as MLOps Platforms
Unique Categories
Azure Machine Learning is categorized as Large Language Model Operationalization (LLMOps), Data Science and Machine Learning Platforms, Generative AI Infrastructure, and Low-Code Machine Learning Platforms
neptune.ai has no unique categories
Reviews
Reviewers' Company Size
Azure Machine Learning
Small-Business(50 or fewer emp.)
Mid-Market(51-1000 emp.)
Enterprise(> 1000 emp.)
neptune.ai
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
28.2%
Computer Software
14.1%
Management Consulting
8.2%
Education Management
5.9%
Higher Education
4.7%
Other
38.8%
Computer Software
27.8%
Research
9.3%
Insurance
9.3%
Biotechnology
9.3%
Program Development
5.6%
Other
38.9%
Alternatives
Discussions
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
What are the MLOps tools?
1 Comment
YT
Common tools are DVC for data versioning, ClearML, AWS Sage Maker, Neptune and Qwak for experiment management, Aporia for model monitoring. Some tools are...Read more
neptune.ai has no more discussions with answers
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