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Compare Datafold and Monte Carlo 
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At a Glance
Market Segments
Mid-Market (54.2% of reviews)
Enterprise (51.1% of reviews)
Entry-Level Pricing
No pricing available
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Datafold
Star Rating
(24)4.5 out of 5
Market Segments
Mid-Market (54.2% of reviews)
Pros & Cons
Not enough data
Entry-Level Pricing
No pricing available
Learn more about DatafoldMonte Carlo
Star Rating
(459)4.4 out of 5
Market Segments
Enterprise (51.1% of reviews)
Pros & Cons
Entry-Level Pricing
Contact Us
Browse all 3 pricing plansSummary
AI Generated Summary
AI-generated. Powered by real user reviews.
- Users report that Monte Carlo excels in data observability with a score of 9.3, while Datafold's score of 8.2 indicates it may not provide the same level of insight into data quality issues. Reviewers mention that Monte Carlo's monitoring capabilities are robust, allowing for real-time tracking of data pipelines.
- Reviewers mention that Datafold shines in data integration with a score of 9.2, surpassing Monte Carlo's score of 8.7. Users on G2 appreciate Datafold's seamless integration with various data sources, making it easier to manage data workflows.
- Users say that Monte Carlo's quality of support is highly rated at 9.3, compared to Datafold's 9.1. Reviewers highlight the responsiveness and helpfulness of Monte Carlo's support team, which enhances the overall user experience.
- G2 users report that Datafold offers superior real-time analytics capabilities with a score of 8.6, while Monte Carlo's score of 7.4 suggests it may lag in this area. Users mention that Datafold's analytics features provide deeper insights into data trends and anomalies.
- Reviewers mention that Monte Carlo's ease of use is rated at 8.4, which is slightly lower than Datafold's 8.8. Users appreciate Datafold's intuitive interface, making it easier for teams to adopt and utilize the software effectively.
- Users report that Monte Carlo's automated workflows score 7.9, while Datafold's score of 7.8 indicates a slight edge in this area. However, reviewers mention that both products could improve their automation features to enhance user efficiency.
Pricing
Entry-Level Pricing
Datafold
No pricing availableMonte Carlo
Start
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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
Free Trial
Datafold
No trial information availableMonte Carlo
No trial information availableRatings
Meets Requirements
8.5
20
8.3
427
Ease of Use
8.8
20
8.2
434
Ease of Setup
7.9
8
8.2
300
Ease of Admin
7.9
8
8.5
159
Quality of Support
9.1
20
9.0
384
Has the product been a good partner in doing business?
8.3
8
9.3
161
Product Direction (% positive)
8.8
20
8.9
423
Features by Category
Functionality
Not enough data
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
Agentic AI - Database Monitoring
Not enough data
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
Data Management
9.2
12
8.5
49
8.3
10
8.5
45
Feature Not Available
8.6
49
Feature Not Available
7.9
47
Agentic AI - DataOps Platforms
Not enough data
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
Analytics
Feature Not Available
7.8
48
8.0
10
7.7
46
Monitoring and Management
8.2
11
9.2
53
9.3
10
7.6
46
Cloud Deployment
9.2
8
7.4
42
9.0
8
7.0
40
Generative AI
Not enough data
6.2
33
Not enough data
6.1
33
Functionality
8.6
12
7.3
287
8.2
12
8.8
317
8.3
12
8.1
291
8.5
12
8.0
295
Management
8.1
12
8.7
313
8.5
11
7.8
283
8.1
12
8.3
305
8.0
11
8.0
301
8.3
11
8.1
305
Generative AI
Not enough data
5.8
227
Agentic AI - Data Observability
Not enough data
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
Functionality
Not enough data
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
Management
Not enough data
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
Generative AI
Not enough data
5.2
142
Not enough data
5.3
142
Categories
Categories
Shared Categories
Datafold and Monte Carlo are categorized as DataOps Platforms and Data Observability
Unique Categories
Datafold has no unique categories
Monte Carlo is categorized as Database Monitoring and Data Quality
Reviews
Reviewers' Company Size
Datafold
Small-Business(50 or fewer emp.)
Mid-Market(51-1000 emp.)
Enterprise(> 1000 emp.)
Monte Carlo
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
29.2%
Computer Software
12.5%
Accounting
8.3%
Wholesale
4.2%
Telecommunications
4.2%
Other
41.7%
Financial Services
14.3%
Information Technology and Services
10.9%
Computer Software
10.7%
Marketing and Advertising
3.8%
Manufacturing
3.6%
Other
56.7%
Alternatives
Discussions
Datafold has no discussions with answers
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
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