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Compare Azure Machine Learning and Palantir Foundry 
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At a Glance
Market Segments
Enterprise (38.8% of reviews)
Small-Business (41.7% of reviews)
Entry-Level Pricing
No pricing available
No pricing available
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 LearningPalantir Foundry
Star Rating
(12)4.1 out of 5
Market Segments
Small-Business (41.7% of reviews)
Pros & Cons
Not enough data
Entry-Level Pricing
No pricing available
Learn more about Palantir FoundrySummary
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 Palantir Foundry falls behind at 7.2. Reviewers mention that Azure's intuitive interface and comprehensive documentation make it easier for teams to get started quickly.
- Reviewers mention that Azure Machine Learning offers superior Quality of Support with a score of 8.6 compared to Palantir Foundry's 7.8. Users on G2 appreciate Azure's responsive customer service and extensive community resources, which help resolve issues more efficiently.
- Users say that Azure Machine Learning shines in Scalability, scoring 8.9, while Palantir Foundry scores 9.2. However, reviewers mention that Azure's managed service capabilities allow for seamless scaling of machine learning models, making it a strong contender for enterprise-level applications.
- Reviewers mention that Azure Machine Learning's Data Ingestion & Wrangling features score 8.7, indicating a robust capability to handle diverse data sources. In contrast, Palantir Foundry's data management features are noted to be less flexible, which can hinder data preparation processes.
- Users report that Azure Machine Learning provides a more comprehensive Model Training experience with a score of 8.7, while Palantir Foundry scores 8.8. Reviewers highlight Azure's pre-built algorithms and user-friendly model training interface as key advantages.
- G2 users mention that Azure Machine Learning's Deployment options are highly rated at 8.8, making it easier to implement models in production. In contrast, Palantir Foundry's deployment capabilities, while effective, are perceived as more complex, which can lead to longer implementation times.
Pricing
Entry-Level Pricing
Azure Machine Learning
No pricing availablePalantir Foundry
No pricing availableFree Trial
Azure Machine Learning
No trial information availablePalantir Foundry
No trial information availableRatings
Meets Requirements
8.5
81
7.8
9
Ease of Use
8.5
80
7.2
9
Ease of Setup
8.3
57
Not enough data
Ease of Admin
8.3
49
Not enough data
Quality of Support
8.6
74
7.8
9
Has the product been a good partner in doing business?
8.6
47
Not enough data
Product Direction (% positive)
9.0
80
6.8
11
Features by Category
Data Source Access
Not enough data
7.8
6
Not enough data
6.7
6
Not enough data
6.4
6
Data Interaction
Not enough data
7.2
6
Not enough data
6.9
6
Not enough data
6.9
6
Not enough data
6.9
6
Not enough data
6.9
6
Not enough data
7.2
6
Not enough data
6.9
6
Not enough data
7.5
6
Data Exporting
Not enough data
6.9
6
Not enough data
7.5
6
Not enough data
7.2
6
Generative AI
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
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
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
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
Analytics
Not enough data
Not enough data
Security
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
Agentic AI - Data Fabric
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
Development
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
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
Not enough data
Not enough data
Security
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Integration Options
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
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
Functionality
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Security
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
Statistical Tool
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Data Analysis
Not enough data
Not enough data
Not enough data
Not enough data
Decision Making
Not enough data
Not enough data
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
Categories
Categories
Shared Categories
Azure Machine Learning and Palantir Foundry are categorized as Data Science and Machine Learning Platforms
Unique Categories
Azure Machine Learning is categorized as MLOps Platforms, Large Language Model Operationalization (LLMOps), Generative AI Infrastructure, and Low-Code Machine Learning Platforms
Palantir Foundry is categorized as Data Preparation, Predictive Analytics, Master Data Management (MDM), Data Fabric, Big Data Integration Platforms, and iPaaS
Reviews
Reviewers' Company Size
Azure Machine Learning
Small-Business(50 or fewer emp.)
Mid-Market(51-1000 emp.)
Enterprise(> 1000 emp.)
Palantir Foundry
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%
Information Technology and Services
25.0%
Computer Software
16.7%
Pharmaceuticals
8.3%
Information Services
8.3%
Government Administration
8.3%
Other
33.3%
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
Palantir Foundry has no discussions with answers
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