Compare Microsoft Fabric and Vertex AI 
At a Glance
Market Segments
Enterprise (39.5% of reviews)
Small-Business (42.0% of reviews)
Entry-Level Pricing
No pricing available
Pay As You Go Per Month
Microsoft Fabric
Star Rating
(39)4.7 out of 5
Market Segments
Enterprise (39.5% of reviews)
Pros & Cons
Entry-Level Pricing
No pricing available
Learn more about Microsoft FabricVertex AI
Star Rating
(652)4.3 out of 5
Market Segments
Small-Business (42.0% of reviews)
Pros & Cons
Entry-Level Pricing
Pay As You Go Per Month
Learn more about Vertex AISummary
AI Generated Summary
AI-generated. Powered by real user reviews.
- G2 reviewers report that Vertex AI excels in managing complex machine learning workflows, with users highlighting its ability to centralize the entire ML lifecycle. This integration simplifies tasks from data preparation to model deployment, making it a preferred choice for those looking to streamline their ML processes.
- Users say Microsoft Fabric shines in its unified platform that seamlessly combines data engineering, ETL, analytics, and visualization. Reviewers appreciate how it integrates various Microsoft services, such as Data Factory and Power BI, which reduces data movement and enhances overall efficiency for data projects.
- According to verified reviews, Vertex AI has a significantly higher overall satisfaction score, indicating that users feel more positively about their experience with the platform. This is reflected in the feedback praising its comprehensive features and ease of use in managing ML tasks.
- Reviewers mention that while Microsoft Fabric offers a robust set of features, some users find it less intuitive compared to Vertex AI. The feedback suggests that the learning curve can be steeper for new users, which may impact day-to-day usability for teams just starting with data engineering.
- G2 reviewers highlight that Vertex AI's integration with Google Cloud enhances its functionality, allowing for a more cohesive experience when building and deploying models. Users appreciate the seamless connection to cloud resources, which simplifies infrastructure management and scalability.
- Users report that Microsoft Fabric's strong support and documentation contribute positively to their experience, with many noting the helpful resources available for troubleshooting and implementation. This support can be a significant advantage for enterprises looking to leverage its capabilities effectively.
Pricing
Entry-Level Pricing
Microsoft Fabric
No pricing availableVertex AI
Try Vertex AI Free
Pay As You Go
Per Month
New customers get $300 in free credits to spend on Vertex AI.
- A unified UI for the entire ML workflow
- Pre-trained APIs for vision, video, natural language, and more
- End-to-end integration for data and AI
Free Trial
Microsoft Fabric
No trial information availableVertex AI
No trial information availableRatings
Meets Requirements
9.4
33
8.6
389
Ease of Use
9.1
34
8.2
400
Ease of Setup
9.2
17
8.1
322
Ease of Admin
9.0
8
7.9
149
Quality of Support
9.2
33
8.1
364
Has the product been a good partner in doing business?
9.8
8
8.3
143
Product Direction (% positive)
10.0
33
9.2
383
Features by Category
Deployment
9.1
20
8.4
76
9.0
18
8.1
78
8.8
18
8.3
76
8.9
18
8.4
76
9.3
19
8.8
75
Deployment
9.0
17
8.5
75
9.3
17
8.3
73
9.3
18
8.4
72
9.2
17
8.6
74
9.3
16
8.7
71
Management
8.9
16
8.2
71
8.9
17
8.5
73
9.1
16
8.0
71
9.1
16
8.1
70
Operations
9.0
16
8.2
70
9.0
17
8.5
71
9.2
18
8.3
71
Management
8.6
16
8.1
69
8.7
15
8.4
72
8.7
15
8.3
70
Generative AI
8.1
14
8.4
37
8.2
14
8.6
37
System
Not enough data
8.2
173
Model Development
Not enough data
8.5
208
Not enough data
7.9
181
Not enough data
8.4
206
Not enough data
8.5
209
Model Development
Not enough data
8.2
167
Machine/Deep Learning Services
Not enough data
8.3
203
Not enough data
8.5
202
Not enough data
8.2
200
Not enough data
8.3
181
Machine/Deep Learning Services
Not enough data
8.5
167
Not enough data
8.5
166
Deployment
Not enough data
8.3
213
Not enough data
8.3
203
Not enough data
8.6
207
Generative AI
Not enough data
8.3
110
Not enough data
8.3
106
Not enough data
8.1
105
Agentic AI - Data Science and Machine Learning Platforms
Not enough data
8.1
38
Not enough data
7.8
37
Not enough data
7.7
38
Not enough data
7.9
35
Not enough data
8.5
37
Not enough data
7.5
36
Not enough data
7.7
36
Scalability and Performance - Generative AI Infrastructure
Not enough data
9.0
31
Not enough data
8.7
32
Not enough data
8.6
31
Cost and Efficiency - Generative AI Infrastructure
Not enough data
8.0
34
Not enough data
7.7
31
Not enough data
8.1
30
Integration and Extensibility - Generative AI Infrastructure
Not enough data
8.5
30
Not enough data
8.3
32
Not enough data
8.5
31
Security and Compliance - Generative AI Infrastructure
Not enough data
8.7
30
Not enough data
8.3
32
Not enough data
8.9
30
Usability and Support - Generative AI Infrastructure
Not enough data
8.2
31
Not enough data
8.5
31
Integration - Machine Learning
Not enough data
8.5
68
Learning - Machine Learning
Not enough data
8.5
66
Not enough data
8.3
65
Not enough data
8.8
66
Large Language Model Operationalization (LLMOps)Hide 15 FeaturesShow 15 Features
Not enough data
9.0
26
Prompt Engineering - Large Language Model Operationalization (LLMOps)
Not enough data
8.8
24
Not enough data
9.0
24
Inference Optimization - Large Language Model Operationalization (LLMOps)
Not enough data
8.8
22
Model Garden - Large Language Model Operationalization (LLMOps)
Not enough data
9.3
25
Custom Training - Large Language Model Operationalization (LLMOps)
Not enough data
9.1
24
Application Development - Large Language Model Operationalization (LLMOps)
Not enough data
9.2
23
Model Deployment - Large Language Model Operationalization (LLMOps)
Not enough data
9.0
23
Not enough data
8.7
22
Guardrails - Large Language Model Operationalization (LLMOps)
Not enough data
8.7
23
Not enough data
8.9
22
Model Monitoring - Large Language Model Operationalization (LLMOps)
Not enough data
8.7
21
Not enough data
9.1
22
Security - Large Language Model Operationalization (LLMOps)
Not enough data
9.1
22
Not enough data
9.0
23
Gateways & Routers - Large Language Model Operationalization (LLMOps)
Not enough data
8.9
22
Customization - AI Agent Builders
Not enough data
8.6
28
Not enough data
7.6
27
Not enough data
8.3
26
Functionality - AI Agent Builders
Not enough data
8.1
27
Not enough data
7.3
27
Not enough data
8.2
26
Not enough data
7.2
27
Data and Analytics - AI Agent Builders
Not enough data
7.8
26
Not enough data
7.9
27
Not enough data
8.1
28
Integration - AI Agent Builders
Not enough data
8.8
28
Not enough data
8.2
30
Not enough data
8.1
28
Not enough data
7.5
27
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
Microsoft Fabric and Vertex AI are categorized as MLOps Platforms
Unique Categories
Microsoft Fabric has no unique categories
Reviews
Reviewers' Company Size
Microsoft Fabric
Small-Business(50 or fewer emp.)
Mid-Market(51-1000 emp.)
Enterprise(> 1000 emp.)
Vertex 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
Insurance
15.8%
Information Technology and Services
15.8%
Manufacturing
7.9%
Education Management
7.9%
Commercial Real Estate
5.3%
Other
47.4%
Computer Software
17.9%
Information Technology and Services
14.3%
Financial Services
6.8%
Retail
3.6%
Hospital & Health Care
3.3%
Other
54.1%
Alternatives
Discussions
Microsoft Fabric has no discussions with answers
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