Compare Databricks and Vertex AI 
At a Glance
Market Segments
Enterprise (45.1% of reviews)
Small-Business (42.0% of reviews)
Entry-Level Pricing
No pricing available
Pay As You Go Per Month
Databricks
Star Rating
(722)4.6 out of 5
Market Segments
Enterprise (45.1% of reviews)
Pros & Cons
Entry-Level Pricing
No pricing available
Learn more about DatabricksVertex 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 Databricks Data Intelligence Platform excels in ease of use, with many users appreciating its intuitive interface and quick implementation process. One user highlighted the platform's integration with external tools like Power BI, which enhances reporting capabilities.
- Users say that Vertex AI shines in centralized management of the machine learning lifecycle, making it easier to build, train, and deploy models. Reviewers have noted that the platform simplifies complex workflows, which is a significant advantage for teams managing multiple ML projects.
- According to verified reviews, Databricks offers superior collaboration features, with users praising its ability to facilitate teamwork across data engineering tasks. The platform's unified data engineering capabilities are frequently mentioned as a key benefit.
- Reviewers mention that Vertex AI's seamless integration with Google Cloud enhances its functionality, allowing for a more streamlined experience when managing data and models. This integration is particularly beneficial for organizations already invested in the Google ecosystem.
- G2 reviewers highlight that Databricks provides robust data analytics and fast calculations, which are crucial for enterprises needing to process large datasets efficiently. Users have expressed satisfaction with the platform's performance in handling complex data tasks.
- Users report that while Vertex AI is effective for small businesses, it may not offer the same level of scalability as Databricks for larger enterprises. Databricks' architecture is designed to support extensive data operations, making it a preferred choice for organizations with significant data needs.
Pricing
Entry-Level Pricing
Databricks
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
Databricks
Free Trial is available
Vertex AI
No trial information availableRatings
Meets Requirements
8.9
567
8.6
389
Ease of Use
8.9
578
8.2
400
Ease of Setup
8.7
450
8.1
322
Ease of Admin
8.3
181
7.9
149
Quality of Support
8.7
542
8.1
364
Has the product been a good partner in doing business?
8.9
185
8.3
143
Product Direction (% positive)
9.6
555
9.2
383
Features by Category
Reports
8.2
11
Not enough data
7.4
9
Not enough data
8.3
11
Not enough data
8.0
9
Not enough data
8.9
14
Not enough data
Data Preparation
8.9
6
Not enough data
8.8
7
Not enough data
Data Modeling and Blending
9.0
8
Not enough data
8.8
7
Not enough data
8.8
8
Not enough data
Generative AI
9.1
9
Not enough data
9.2
6
Not enough data
Agentic AI - Analytics Platforms
9.3
7
Not enough data
8.7
5
Not enough data
8.3
6
Not enough data
8.3
6
Not enough data
8.3
7
Not enough data
9.7
6
Not enough data
9.3
5
Not enough data
Self Service
7.7
10
Not enough data
7.7
10
Not enough data
8.5
11
Not enough data
8.1
12
Not enough data
8.2
11
Not enough data
7.9
8
Not enough data
Deployment & Integration - Analytics Platforms
8.1
7
Not enough data
9.2
8
Not enough data
8.6
6
Not enough data
9.2
6
Not enough data
Advanced Analytics
8.3
11
Not enough data
8.2
11
Not enough data
9.0
12
Not enough data
Performance & Scalability - Analytics Platforms
8.8
7
Not enough data
Not enough data
Not enough data
Advanced Analytics & Modeling - Analytics Platforms
9.0
8
Not enough data
8.5
8
Not enough data
8.6
6
Not enough data
Agentic AI Capabilities - Analytics Platforms
8.6
7
Not enough data
8.8
7
Not enough data
8.9
6
Not enough data
8.6
7
Not enough data
Personalized Intelligence - Analytics Platforms
9.2
6
Not enough data
7.9
8
Not enough data
8.3
7
Not enough data
Building Reports
9.0
148
Not enough data
8.6
143
Not enough data
7.6
123
Not enough data
8.4
144
Not enough data
Administration
8.8
99
Not enough data
8.6
93
Not enough data
9.0
94
Not enough data
8.7
97
Not enough data
Management
8.4
87
Not enough data
8.6
86
Not enough data
8.6
87
Not enough data
8.5
87
Not enough data
8.8
86
Not enough data
Compliance
8.8
98
Not enough data
8.7
92
Not enough data
8.9
94
Not enough data
8.7
94
Not enough data
Security
9.0
86
Not enough data
8.9
85
Not enough data
8.8
86
Not enough data
Data Quality
9.0
94
Not enough data
8.7
93
Not enough data
9.0
93
Not enough data
Maintainence
8.7
85
Not enough data
8.8
83
Not enough data
Generative AI
8.4
82
Not enough data
8.5
81
Not enough data
Agentic AI - Data Governance
8.5
10
Not enough data
8.8
11
Not enough data
9.0
14
Not enough data
8.5
12
Not enough data
9.0
12
Not enough data
9.0
13
Not enough data
Management
8.6
171
Not enough data
8.5
164
Not enough data
Functionality
9.1
172
Not enough data
9.2
174
Not enough data
9.1
172
Not enough data
9.0
167
Not enough data
9.1
167
Not enough data
Deployment
8.9
64
8.4
76
8.9
64
8.1
78
8.9
66
8.3
76
8.9
66
8.4
76
9.0
67
8.8
75
Deployment
8.9
61
8.5
75
8.8
62
8.3
73
8.6
66
8.4
72
8.9
65
8.6
74
9.0
66
8.7
71
Management
9.0
63
8.2
71
8.8
65
8.5
73
9.2
61
8.0
71
8.9
68
8.1
70
Operations
8.7
62
8.2
70
8.8
61
8.5
71
9.0
62
8.3
71
Management
8.8
60
8.1
69
8.7
61
8.4
72
9.1
59
8.3
70
Generative AI
8.3
28
8.4
37
8.3
28
8.6
37
System
8.9
180
8.2
173
Model Development
8.8
182
8.5
208
7.9
187
7.9
181
8.3
186
8.4
206
8.7
190
8.5
209
Model Development
8.5
172
8.2
167
Machine/Deep Learning Services
8.2
174
8.3
203
8.5
180
8.5
202
8.3
172
8.2
200
8.2
172
8.3
181
Machine/Deep Learning Services
8.4
158
8.5
167
8.4
160
8.5
166
Deployment
8.5
178
8.3
213
8.7
180
8.3
203
9.0
187
8.6
207
Generative AI
8.2
86
8.3
110
8.4
87
8.3
106
7.6
86
8.1
105
Agentic AI - Data Science and Machine Learning Platforms
9.2
8
8.1
38
8.3
7
7.8
37
8.1
8
7.7
38
8.6
7
7.9
35
9.2
10
8.5
37
9.2
6
7.5
36
9.3
7
7.7
36
Data Management
9.0
185
|
Verified
Not enough data
8.9
180
|
Verified
Not enough data
8.9
179
|
Verified
Not enough data
8.8
180
|
Verified
Not enough data
8.7
167
|
Verified
Not enough data
9.1
171
|
Verified
Not enough data
Integration
8.8
168
|
Verified
Not enough data
8.8
166
|
Verified
Not enough data
9.1
174
|
Verified
Not enough data
Deployment
7.9
156
|
Verified
Not enough data
9.1
178
|
Verified
Not enough data
Performance
9.1
177
|
Verified
Not enough data
Security
8.8
168
|
Verified
Not enough data
8.9
168
|
Verified
Not enough data
Generative AI
8.5
76
Not enough data
8.4
75
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
Maintenance
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
Data Management
9.0
68
Not enough data
8.7
69
Not enough data
8.8
68
Not enough data
9.1
68
Not enough data
Agentic AI - DataOps Platforms
9.1
9
Not enough data
8.8
7
Not enough data
8.9
9
Not enough data
9.0
8
Not enough data
9.4
8
Not enough data
Analytics
8.8
67
Not enough data
8.5
67
Not enough data
Monitoring and Management
8.6
67
Not enough data
8.6
68
Not enough data
Cloud Deployment
9.0
63
Not enough data
9.0
64
Not enough data
Generative AI
8.9
36
Not enough data
8.9
36
Not enough data
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
Traffic Management & Performance - AI Gateways
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Governance & Observability - AI Gateways
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Database
8.7
236
Not enough data
8.9
243
Not enough data
9.3
256
Not enough data
Integrations
8.3
220
Not enough data
9.1
256
Not enough data
Platform
9.0
246
Not enough data
8.8
246
Not enough data
9.1
256
Not enough data
Processing
9.0
242
Not enough data
9.0
242
Not enough data
Data Transformation
8.8
226
Not enough data
9.1
232
Not enough data
Connectivity
8.7
206
Not enough data
9.2
230
Not enough data
9.0
220
Not enough data
9.2
230
Not enough data
Operations
8.4
227
Not enough data
8.8
224
Not enough data
8.6
212
Not enough data
8.6
209
Not enough data
9.3
229
Not enough data
Building Reports
9.0
148
Not enough data
8.6
143
Not enough data
7.6
123
Not enough data
8.4
144
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
Databricks and Vertex AI are categorized as MLOps Platforms, Data Science and Machine Learning Platforms, Generative AI Infrastructure, and AI Agent Builders
Unique Categories
Databricks is categorized as DataOps Platforms, Data Governance, ETL Tools, Big Data Processing and Distribution, Big Data Analytics, Analytics Platforms, Data Warehouse, Database Management Systems (DBMS), and AI Gateways
Vertex AI is categorized as Large Language Model Operationalization (LLMOps), Machine Learning, and Low-Code Machine Learning Platforms
Reviews
Reviewers' Company Size
Databricks
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
Information Technology and Services
15.4%
Financial Services
10.6%
Computer Software
8.5%
Consulting
3.7%
Hospital & Health Care
3.7%
Other
57.9%
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
What is Lakehouse in Databricks?
4 Comments
BR
Lakehouse is a new and open data management architecture solution that combines the best features of the data lake and warehouse.Read more
What are the features of Databricks?
4 Comments
SA
It supports large amount of data with capability to write code in SQL, Spark, Python and R.
In the backend, it stored the data in the parquet file which...Read more
What is Databricks unified analytics platform?
3 Comments
CA
Databricks' Unified Data Analytics Platform helps organizations accelerate innovation by unifying data science with engineering and business. With Databricks...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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