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Compare Monte Carlo and decube 
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At a Glance
Market Segments
Enterprise (51.2% of reviews)
Mid-Market (36.0% of reviews)
Entry-Level Pricing
Contact Us
Free Per Year
Monte Carlo
Star Rating
(462)4.4 out of 5
Market Segments
Enterprise (51.2% 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.
- Users report that Monte Carlo excels in Data Quality Monitoring with a score of 8.9, while decube shines in Real-time Analytics with a score of 8.7. Reviewers mention that Monte Carlo's robust monitoring features help in identifying data issues quickly, whereas decube's real-time analytics capabilities allow for immediate insights into data performance.
- Reviewers mention that decube offers superior Data Quality and Cleansing features, scoring 9.6 compared to Monte Carlo's 8.9. Users say that decube's automated workflows and data transformation tools significantly streamline the data cleansing process, making it easier for teams to maintain high data quality.
- G2 users highlight that Monte Carlo's Data Lineage functionality is impressive, scoring 9.6, while decube also performs well with a score of 9.2. Users report that Monte Carlo provides clear visibility into data flows, which is crucial for compliance and auditing, whereas decube's lineage features are praised for their user-friendly interface.
- Users on G2 indicate that Monte Carlo's Quality of Support is rated at 9.3, slightly lower than decube's 9.5. Reviewers mention that decube's support team is highly responsive and knowledgeable, which enhances the overall user experience, especially during setup and troubleshooting.
- Reviewers say that Monte Carlo's Ease of Use is rated at 8.4, while decube scores higher at 9.4. Users report that decube's intuitive interface and straightforward setup process make it more accessible for teams without extensive technical expertise.
- Users report that both products have strong Compliance Management features, with Monte Carlo scoring 9.5 and decube slightly lower at 9.3. Reviewers mention that Monte Carlo's focus on sensitive data compliance is particularly beneficial for organizations in regulated industries, while decube's policy enforcement tools are effective for general compliance needs.
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
430
9.4
23
Ease of Use
8.2
437
9.4
24
Ease of Setup
8.2
303
9.4
11
Ease of Admin
8.5
160
8.8
7
Quality of Support
9.0
386
9.4
23
Has the product been a good partner in doing business?
9.3
163
9.2
6
Product Direction (% positive)
8.9
426
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
257
Not enough data
8.8
258
Not enough data
7.8
235
Not enough data
8.3
244
Not enough data
7.7
239
Not enough data
7.4
241
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
49
Not enough data
8.5
45
Not enough data
8.6
49
Not enough data
7.9
47
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
48
Not enough data
7.7
46
Not enough data
Monitoring and Management
9.2
53
Not enough data
7.6
46
Not enough data
Cloud Deployment
7.4
42
Not enough data
7.0
40
Not enough data
Generative AI
6.2
33
Feature Not Available
6.1
33
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
7.3
287
9.0
17
8.8
318
9.6
19
8.1
291
8.8
17
8.0
295
9.3
19
Management
8.7
313
9.1
19
7.8
284
9.1
18
8.3
307
9.0
17
8.0
301
9.2
19
8.1
307
8.8
19
Generative AI
5.8
227
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
184
Not enough data
6.4
171
Not enough data
6.6
166
Not enough data
6.0
161
Not enough data
6.4
162
Not enough data
Management
7.2
167
Not enough data
7.5
167
Not enough data
7.9
165
Not enough data
7.4
172
Not enough data
7.5
167
Not enough data
Generative AI
5.2
142
Not enough data
5.3
142
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
10.9%
Computer Software
10.6%
Marketing and Advertising
3.8%
Manufacturing
3.5%
Other
57.0%
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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