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Compare Anomalo and Monte Carlo 
At a Glance
Market Segments
Enterprise (50.0% of reviews)
Enterprise (50.7% of reviews)
Entry-Level Pricing
Contact Us 10000 Tables Per Year
Contact Us
Anomalo
Star Rating
(41)4.4 out of 5
Market Segments
Enterprise (50.0% of reviews)
Pros & Cons
Not enough data
Entry-Level Pricing
Contact Us 10000 Tables Per Year
Learn more about AnomaloMonte Carlo
Star Rating
(487)4.3 out of 5
Market Segments
Enterprise (50.7% 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, boasting a significantly higher G2 Score compared to Anomalo. Users appreciate its real-time alerts for data quality issues, which have greatly improved their ability to address problems proactively.
- Users say that Monte Carlo's implementation process is intuitive and efficient, with many highlighting the tool's continuous updates that enhance usability. In contrast, while Anomalo is noted for its quick setup of checks, some users feel it lacks the depth of features that Monte Carlo offers.
- Reviewers mention that both products provide strong customer support, but Monte Carlo's users have expressed a higher level of satisfaction with their overall partnership experience, indicating a more robust support system that helps them navigate challenges effectively.
- According to verified reviews, Monte Carlo shines in data observability, receiving praise for its specialized focus and proactive monitoring capabilities. Users have noted that it allows them to maintain a better sense of data reliability, while Anomalo's users appreciate its customizable monitoring options but feel it may not be as comprehensive.
- Users highlight that Anomalo's native scans require minimal effort to set up, making it user-friendly for quick checks. However, Monte Carlo's advanced features, such as its ability to alert users before stakeholders notice data issues, provide a more proactive approach to data management.
- Reviewers indicate that while both products cater to enterprise-level needs, Monte Carlo has a more substantial presence in the market, reflected in its higher total number of reviews. This suggests a broader user base and potentially more reliable feedback compared to Anomalo, which has fewer reviews and a smaller footprint.
Pricing
Entry-Level Pricing
Anomalo
Custom
Contact Us
10000 Tables Per Year
Free pilot. Secure In-VPC deployment. Contact us to get started.
- Automated data monitoring
- Simple installation
- Visualizations for reporting and explainability
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
Free Trial
Anomalo
Free Trial is available
Monte Carlo
No trial information availableRatings
Meets Requirements
8.2
36
8.3
445
Ease of Use
8.5
36
8.2
452
Ease of Setup
9.4
14
8.1
317
Ease of Admin
8.1
15
8.5
161
Quality of Support
9.0
35
9.0
399
Has the product been a good partner in doing business?
8.9
15
9.2
164
Product Direction (% positive)
8.8
29
8.9
442
Features by Category
Functionality
8.1
7
9.0
260
7.9
7
8.8
261
6.0
5
7.8
237
6.9
6
8.3
246
7.2
6
7.7
241
7.9
7
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
Not enough data
8.6
52
Not enough data
8.5
48
Not enough data
8.6
52
Not enough data
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
Not enough data
7.9
51
Not enough data
7.7
48
Monitoring and Management
Not enough data
9.2
56
Not enough data
7.7
49
Cloud Deployment
Not enough data
7.5
44
Not enough data
7.1
42
Generative AI
Not enough data
6.3
35
Not enough data
6.2
35
Functionality
Not enough data
7.4
292
9.4
6
8.8
328
7.8
6
8.1
296
8.7
5
8.0
304
Management
9.3
7
8.7
326
8.0
5
7.7
286
8.6
6
8.3
317
Not enough data
8.0
308
7.1
7
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
8.7
14
8.1
189
Feature Not Available
6.4
174
Feature Not Available
6.7
169
6.9
7
6.1
164
7.8
10
6.4
165
Management
8.7
15
7.2
169
7.8
18
7.5
169
8.5
14
7.9
168
8.6
16
7.4
176
7.4
12
7.5
169
Generative AI
Not enough data
5.2
145
Not enough data
5.3
145
Categories
Categories
Shared Categories
Anomalo and Monte Carlo are categorized as Data Quality, Data Observability, and Database Monitoring
Reviews
Reviewers' Company Size
Anomalo
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
Financial Services
22.5%
Information Technology and Services
12.5%
Computer Software
12.5%
Leisure, Travel & Tourism
7.5%
Internet
7.5%
Other
37.5%
Financial Services
13.9%
Computer Software
11.3%
Information Technology and Services
11.1%
Marketing and Advertising
3.6%
Manufacturing
3.4%
Other
56.5%
Discussions
Anomalo 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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