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Compare Datafold and Monte Carlo 
At a Glance
Market Segments
Mid-Market (54.2% of reviews)
Enterprise (50.9% of reviews)
Entry-Level Pricing
No pricing available
Contact Us
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
(488)4.3 out of 5
Market Segments
Enterprise (50.9% of reviews)
Pros & Cons
Entry-Level Pricing
Contact Us
Browse all 3 pricing plansSummary
AI Generated Summary
AI-generated. Powered by real user reviews.
- G2 reviewers report that Monte Carlo excels in overall user satisfaction, reflected in its significantly higher G2 Score compared to Datafold. Users appreciate its focus on data observability, with features like real-time alerts that enhance awareness of data quality issues, allowing teams to address problems proactively.
- According to verified reviews, Monte Carlo has a much larger user base, with 460 total reviews compared to Datafold's 24. This suggests a more established presence in the market, giving potential buyers confidence in the product's reliability and support.
- Users say that Datafold shines in ease of use, with many finding it straightforward for automating data testing and integrating with tools like GitHub. Reviewers highlight its ability to validate SQL code changes automatically, which streamlines workflows and reduces manual effort.
- Reviewers mention that while Monte Carlo offers robust data monitoring capabilities, it is constantly evolving, making it more intuitive over time. Users have noted that the tool's ongoing feature updates significantly improve their experience, enhancing data reliability and observability.
- Users highlight that Datafold's workflow is impressive, particularly in simplifying traditional data transfer processes. This has garnered positive feedback from teams who appreciate the efficiency it brings to their operations, making it a strong contender for those focused on workflow optimization.
- According to recent user feedback, Monte Carlo's quality of support is highly rated, with users feeling that the product has been a good partner in their business. In contrast, while Datafold also receives positive remarks for support, it does not match the same level of satisfaction reported by Monte Carlo users.
Pricing
Entry-Level Pricing
Datafold
No pricing availableMonte 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
Free Trial
Datafold
No trial information availableMonte Carlo
No trial information availableRatings
Meets Requirements
8.5
20
8.3
446
Ease of Use
8.8
20
8.2
453
Ease of Setup
7.9
8
8.1
318
Ease of Admin
7.9
8
8.5
161
Quality of Support
9.1
20
9.0
400
Has the product been a good partner in doing business?
8.3
8
9.2
164
Product Direction (% positive)
8.8
20
8.9
443
Features by Category
Functionality
Not enough data
9.0
260
Not enough data
8.8
261
Not enough data
7.8
237
Not enough data
8.3
246
Not enough data
7.7
241
Not enough data
7.4
243
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
8.6
52
8.3
10
8.5
48
Feature Not Available
8.6
52
Feature Not Available
7.9
50
Agentic AI - DataOps Platforms
Not enough data
7.6
7
Not enough data
6.7
6
Not enough data
6.9
6
Not enough data
6.9
6
Not enough data
6.9
6
Analytics
Feature Not Available
7.9
51
8.0
10
7.7
48
Monitoring and Management
8.2
11
9.2
56
9.3
10
7.7
49
Cloud Deployment
9.2
8
7.5
44
9.0
8
7.1
42
Generative AI
Not enough data
6.3
35
Not enough data
6.2
35
Functionality
8.6
12
7.4
292
8.2
12
8.8
329
8.3
12
8.1
297
8.5
12
8.0
305
Management
8.1
12
8.7
327
8.5
11
7.7
286
8.1
12
8.3
318
8.0
11
8.0
309
8.3
11
8.1
313
Generative AI
Not enough data
5.8
232
Agentic AI - Data Observability
Not enough data
6.3
28
Not enough data
6.4
28
Not enough data
6.8
28
Not enough data
6.4
26
Not enough data
6.8
30
Functionality
Not enough data
8.1
189
Not enough data
6.4
174
Not enough data
6.7
169
Not enough data
6.1
164
Not enough data
6.4
165
Management
Not enough data
7.2
169
Not enough data
7.5
169
Not enough data
7.9
168
Not enough data
7.4
176
Not enough data
7.5
169
Generative AI
Not enough data
5.2
145
Not enough data
5.3
145
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
13.9%
Information Technology and Services
11.3%
Computer Software
11.3%
Marketing and Advertising
3.6%
Manufacturing
3.4%
Other
56.4%
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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