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Compare Monte Carlo and decube 
At a Glance
Market Segments
Enterprise (50.8% of reviews)
Mid-Market (36.0% of reviews)
Entry-Level Pricing
Contact Us
Free Per Year
Monte Carlo
Star Rating
(469)4.4 out of 5
Market Segments
Enterprise (50.8% of reviews)
Pros & Cons
Entry-Level Pricing
Contact Us
Browse all 3 pricing plansdecube
Star Rating
(25)4.6 out of 5
Market Segments
Mid-Market (36.0% of reviews)
Pros & Cons
Entry-Level Pricing
Free Per Year
Browse all 3 pricing plansSummary
AI Generated Summary
AI-generated. Powered by real user reviews.
- G2 reviewers report that Monte Carlo excels in data observability, with users highlighting its real-time alerts that significantly enhance awareness of ongoing data issues. This proactive approach allows teams to address problems before stakeholders notice them, which is a game-changer for data reliability.
- Users say that decube stands out for its intuitive design and ease of use, making it a favorite among those who appreciate a straightforward setup. Reviewers have praised its simple dashboard and automated alerts, which facilitate clear insights into data quality without overwhelming complexity.
- According to verified reviews, Monte Carlo has a strong presence in the enterprise market, with over half of its reviews coming from larger organizations. This suggests that it is well-suited for complex data environments, although it may not cater as effectively to smaller businesses compared to decube.
- Reviewers mention that while decube has a higher star rating, its limited number of reviews may not fully capture the breadth of user experiences. In contrast, Monte Carlo's extensive feedback base provides a more reliable picture of its performance and user satisfaction.
- Users highlight that Monte Carlo is continuously evolving, with frequent updates that enhance its functionality. This commitment to improvement is reflected in user comments about its growing intuitiveness and feature set, making it a robust choice for teams focused on data monitoring.
- G2 reviewers note that decube's focus on data trust and governance is particularly beneficial for organizations looking to ensure data reliability in AI and analytics contexts. Its comprehensive approach helps users maintain data quality and discoverability, which is crucial for modern data stacks.
Pricing
Entry-Level Pricing
Monte Carlo
Start
Contact Us
All you need to make data reliable for one team
- Monitoring for Data Warehouse, Business Intelligence, ETL
- Incident triaging, troubleshooting and root cause analysis
- Lineage
decube
Community Edition
Free
Per Year
A free-forever tier of our platform for small teams with unlimited data cataloging and data quality monitoring for up to 25 tables.
- Unlimited data cataloging
- 25 tables monitored with unlimited tests per table.
- Includes Business Glossary
Free Trial
Monte Carlo
No trial information availabledecube
Free Trial is available
Ratings
Meets Requirements
8.3
436
9.4
23
Ease of Use
8.2
443
9.4
24
Ease of Setup
8.2
308
9.4
11
Ease of Admin
8.5
160
8.8
7
Quality of Support
9.0
390
9.4
23
Has the product been a good partner in doing business?
9.3
163
9.2
6
Product Direction (% positive)
8.9
432
9.5
24
Features by Category
Data Governance
Not enough data
9.5
10
Not enough data
9.8
10
Not enough data
9.7
10
Data Preparation
Not enough data
9.8
10
Not enough data
Feature Not Available
Not enough data
Feature Not Available
Not enough data
Feature Not Available
Collaboration
Not enough data
9.2
11
Not enough data
9.5
11
Not enough data
9.7
11
Not enough data
9.7
11
Artificial Intelligence
Not enough data
9.7
11
Not enough data
8.8
10
Not enough data
Feature Not Available
Generative AI
Not enough data
Not enough data
Not enough data
Feature Not Available
Agentic AI - Machine Learning Data Catalog
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
Administration
Not enough data
8.3
6
Not enough data
8.8
7
Not enough data
9.0
7
Not enough data
9.3
7
Management
Not enough data
9.3
9
Not enough data
9.6
9
Not enough data
9.6
9
Not enough data
9.7
6
Not enough data
9.6
8
Compliance
Not enough data
9.2
8
Not enough data
9.3
7
Not enough data
9.3
7
Not enough data
9.4
6
Security
Not enough data
9.4
9
Not enough data
9.6
8
Not enough data
9.6
9
Data Quality
Not enough data
Feature Not Available
Not enough data
9.2
6
Not enough data
9.6
8
Maintainence
Not enough data
9.4
9
Not enough data
9.0
8
Generative AI
Not enough data
Feature Not Available
Not enough data
Feature Not Available
Agentic AI - Data Governance
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
Functionality
9.0
260
Not enough data
8.8
261
Not enough data
7.8
238
Not enough data
8.3
247
Not enough data
7.7
242
Not enough data
7.4
244
Not enough data
Agentic AI - Database Monitoring
7.1
13
Not enough data
6.9
13
Not enough data
6.9
13
Not enough data
7.1
13
Not enough data
6.8
12
Not enough data
6.5
13
Not enough data
7.1
13
Not enough data
Data Management
8.5
51
Not enough data
8.4
47
Not enough data
8.6
51
Not enough data
7.9
49
Not enough data
Agentic AI - DataOps Platforms
7.2
6
Not enough data
6.0
5
Not enough data
6.3
5
Not enough data
6.3
5
Not enough data
6.3
5
Not enough data
Analytics
7.8
50
Not enough data
7.7
47
Not enough data
Monitoring and Management
9.2
55
Not enough data
7.6
48
Not enough data
Cloud Deployment
7.4
43
Not enough data
7.0
41
Not enough data
Generative AI
6.2
34
Feature Not Available
6.1
34
Feature Not Available
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
Reporting
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
Functionality
7.4
291
9.0
17
8.8
323
9.6
19
8.1
294
8.8
17
8.0
299
9.3
19
Management
8.7
318
9.1
19
7.7
287
9.1
18
8.3
311
9.0
17
8.0
305
9.2
19
8.1
312
8.8
19
Generative AI
5.8
231
Feature Not Available
Agentic AI - Data Observability
6.1
27
Not enough data
6.2
27
Not enough data
6.7
27
Not enough data
6.4
26
Not enough data
6.7
29
Not enough data
Functionality
8.1
187
Not enough data
6.4
174
Not enough data
6.7
169
Not enough data
6.1
164
Not enough data
6.4
165
Not enough data
Management
7.2
169
Not enough data
7.5
169
Not enough data
7.9
167
Not enough data
7.4
175
Not enough data
7.5
169
Not enough data
Generative AI
5.2
145
Not enough data
5.3
145
Not enough data
Categories
Categories
Shared Categories
Monte Carlo and decube are categorized as DataOps Platforms and Data Observability
Unique Categories
Monte Carlo is categorized as Database Monitoring and Data Quality
decube is categorized as Machine Learning Data Catalog, Data Governance, and Active Metadata Management
Reviews
Reviewers' Company Size
Monte Carlo
Small-Business(50 or fewer emp.)
Mid-Market(51-1000 emp.)
Enterprise(> 1000 emp.)
decube
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
Financial Services
14.2%
Information Technology and Services
11.2%
Computer Software
10.7%
Marketing and Advertising
3.7%
Manufacturing
3.5%
Other
56.7%
Information Technology and Services
24.0%
Financial Services
16.0%
Automotive
12.0%
Telecommunications
8.0%
Computer Software
8.0%
Other
32.0%
Alternatives
Discussions
What is Monte Carlo software?
1 Comment
MV
Monte Carlo is a fully automated, end-to-end data observability platform that helps data engineering teams reduce time to detection and resolution for data...Read more
Monte Carlo has no more discussions with answers
decube has no discussions with answers
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