Compare Qubole and Vertex AI 
At a Glance
Market Segments
Enterprise (51.1% of reviews)
Small-Business (42.0% of reviews)
Entry-Level Pricing
30 day free trial
Pay As You Go Per Month
Qubole
Star Rating
(259)4.0 out of 5
Market Segments
Enterprise (51.1% of reviews)
Pros & Cons
Not enough data
Entry-Level Pricing
30 day free trial
Browse all 3 pricing plansVertex 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 appreciating its ability to centralize the entire ML lifecycle. This integration simplifies tasks from data preparation to model deployment, making it a favorite for those looking for a streamlined experience.
- Users say that Qubole offers a robust self-service platform for big data management, particularly highlighting its ease of use with AWS and Hadoop's HDFS system. Reviewers find it particularly effective for ETL processes, showcasing its strengths in handling large datasets programmatically.
- According to verified reviews, Vertex AI's seamless integration with Google Cloud is a significant advantage, allowing users to manage their ML projects in one organized platform. This feature is particularly beneficial for teams looking to reduce the complexity of their workflows.
- Reviewers mention that while Qubole has innovative features, some users find it challenging to configure for specific workflows. Despite this, many appreciate its user-friendly interface and the ability to manage data effectively, which can be a strong point for teams familiar with big data environments.
- G2 reviewers highlight that Vertex AI's recent updates and user feedback indicate a strong commitment to improving the platform, with users noting enhancements in model training and monitoring capabilities. This responsiveness to user needs contributes to its high satisfaction ratings.
- Users report that Qubole's strengths lie in its data ingestion and wrangling capabilities, with many praising its intuitive features that make managing big data straightforward. However, it faces stiff competition from Vertex AI, which offers a more comprehensive solution for machine learning projects.
Pricing
Entry-Level Pricing
Qubole
Free Trial
30 day free trial
Free access to Qubole for 30 days to build data pipelines, bring machine learning to production, and analyze any data type from any data source. Get started today: https://www.qubole.com/free-trial/
Vertex 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
Qubole
Free Trial is available
Vertex AI
No trial information availableRatings
Meets Requirements
8.4
211
8.6
389
Ease of Use
7.8
212
8.2
400
Ease of Setup
7.6
60
8.1
322
Ease of Admin
7.6
58
7.9
149
Quality of Support
7.9
177
8.1
364
Has the product been a good partner in doing business?
8.1
61
8.3
143
Product Direction (% positive)
7.1
208
9.2
383
Features by Category
Deployment
Not enough data
8.4
76
Not enough data
8.1
78
Not enough data
8.3
76
Not enough data
8.4
76
Not enough data
8.8
75
Deployment
Not enough data
8.5
75
Not enough data
8.3
73
Not enough data
8.4
72
Not enough data
8.6
74
Not enough data
8.7
71
Management
Not enough data
8.2
71
Not enough data
8.5
73
Not enough data
8.0
71
Not enough data
8.1
70
Operations
Not enough data
8.2
70
Not enough data
8.5
71
Not enough data
8.3
71
Management
Not enough data
8.1
69
Not enough data
8.4
72
Not enough data
8.3
70
Generative AI
Not enough data
8.4
37
Not enough data
8.6
37
System
9.0
7
8.2
173
Model Development
7.9
7
8.5
208
6.7
5
7.9
181
8.0
5
8.4
206
8.1
6
8.5
209
Model Development
8.3
5
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
8.0
5
8.5
166
Deployment
9.0
5
8.3
213
8.3
6
8.3
203
8.8
7
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
Database
8.0
64
Not enough data
8.4
83
Not enough data
8.3
79
Not enough data
Integrations
8.6
81
Not enough data
8.6
87
Not enough data
Platform
8.3
72
Not enough data
8.3
82
Not enough data
8.6
87
Not enough data
Processing
8.5
85
Not enough data
8.3
82
Not enough data
Data Transformation
8.0
119
|
Verified
Not enough data
8.8
10
|
Verified
Not enough data
Connectivity
8.8
107
|
Verified
Not enough data
8.6
123
|
Verified
Not enough data
8.3
108
|
Verified
Not enough data
8.5
113
|
Verified
Not enough data
Operations
7.0
104
|
Verified
Not enough data
7.8
116
|
Verified
Not enough data
7.2
75
|
Verified
Not enough data
7.8
69
|
Verified
Not enough data
8.8
11
|
Verified
Not enough data
Building Reports
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Platform
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
Categories
Categories
Shared Categories
Qubole and Vertex AI are categorized as Data Science and Machine Learning Platforms
Unique Categories
Qubole is categorized as Big Data Processing and Distribution, Big Data Analytics, and Big Data Integration Platforms
Vertex AI is categorized as Large Language Model Operationalization (LLMOps), Generative AI Infrastructure, MLOps Platforms, Machine Learning, AI Agent Builders, and Low-Code Machine Learning Platforms
Reviews
Reviewers' Company Size
Qubole
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
Computer Software
19.4%
Information Technology and Services
14.3%
Marketing and Advertising
12.2%
Internet
6.8%
Leisure, Travel & Tourism
4.2%
Other
43.0%
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
1.What is the best way to pass external spark packages in Qubole Notebooks?;
2 Comments
RR
It give clarity about financial data which help at business level to expansion and cut down cost , to review apex and opex and budgeting prospectiveRead more
How do i integrate more charts or graphs in Qubole Notebooks in SQL for quick visualization
2 Comments
RR
we can create schma by using postman and multiple chart which to help to analysis the data and clarity about the decision making of further processRead more
How is Qubole going to survive the Kubernates wave in bigdata processing.
1 Comment
JV
Qubole supports Kubernetes for its control plane—it was announced in April 2019 during the Google Next '19 conference. Please sign up to our newsletter for...Read more
What is Google Cloud AI Platform used for?
3 Comments
AO
Vertex AI is Google Cloud’s managed machine learning platform. It’s used to build, train, and deploy ML models at scale. It unifies data, AutoML, and custom...Read more
What software libraries does cloud ML engine support?
3 Comments
AO
Cloud ML Engine (now part of Vertex AI) supports popular ML frameworks and libraries such as TensorFlow, PyTorch, Scikit‑learn, and XGBoost. This flexibility...Read more
What is Google AI platform?
2 Comments
ZM
The Google AI Platform is a comprehensive set of tools and services provided by Google Cloud to develop, deploy, and manage artificial intelligence. It...Read more
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