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Compare Bigeye and Monte Carlo 
Featured Products
At a Glance
Market Segments
Small-Business (54.5% of reviews)
Enterprise (50.7% of reviews)
Entry-Level Pricing
Contact Us
Contact Us
Bigeye
Star Rating
(22)4.1 out of 5
Market Segments
Small-Business (54.5% of reviews)
Pros & Cons
Entry-Level Pricing
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Learn more about BigeyeMonte 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 data observability, receiving high praise for its real-time alerts that enhance awareness of data quality issues. Users appreciate how these alerts allow teams to proactively address problems before they escalate, significantly improving data reliability.
- Users say that Bigeye offers a user-friendly experience, with its core concepts being simple and easy to grasp. This accessibility is complemented by more technical features, making it a versatile choice for both novice and advanced users looking to monitor data quality.
- According to verified reviews, Monte Carlo's implementation process is notably efficient, with many users highlighting its intuitive onboarding features. This ease of setup helps teams quickly integrate the tool into their workflows, enhancing overall productivity.
- Reviewers mention that while Bigeye provides solid functionality for creating freshness and volume checks, it may not match the comprehensive monitoring capabilities of Monte Carlo. Users have noted that Monte Carlo's specialized focus on data observability leads to a more robust solution for ongoing data management.
- G2 reviewers highlight the quality of support provided by Monte Carlo, which is rated highly by users. Many have expressed satisfaction with the responsiveness and helpfulness of the support team, contributing to a positive overall experience with the product.
- Users report that Bigeye shines in its ability to perform custom checks via SQL, which adds significant value for users who need tailored monitoring solutions. However, some users feel that its overall feature set may not be as comprehensive as that of Monte Carlo, particularly in the realm of data observability.
Pricing
Entry-Level Pricing
Bigeye
Enterprise Starter
Contact Us
- End-to-end lineage
- Cloud, on prem, and hybrid support
- Cross-source columnar mapping
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
Bigeye
No trial information availableMonte Carlo
No trial information availableRatings
Meets Requirements
8.0
22
8.3
445
Ease of Use
8.5
22
8.2
452
Ease of Setup
8.1
12
8.1
317
Ease of Admin
7.2
12
8.5
161
Quality of Support
8.2
20
9.0
399
Has the product been a good partner in doing business?
8.3
12
9.2
164
Product Direction (% positive)
7.8
21
8.9
442
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
8.3
5
8.6
52
8.3
5
8.5
48
8.7
5
8.6
52
9.3
5
7.9
50
Agentic AI - DataOps Platforms
Not enough data
7.6
7
Feature Not Available
6.7
6
Not enough data
6.9
6
Not enough data
6.9
6
Feature Not Available
6.9
6
Analytics
9.0
5
7.9
51
9.7
5
7.7
48
Monitoring and Management
9.3
5
9.2
56
Not enough data
7.7
49
Cloud Deployment
9.0
5
7.5
44
9.0
5
7.1
42
Generative AI
Feature Not Available
6.3
35
Not enough data
6.2
35
Functionality
8.2
19
7.4
292
9.0
19
8.8
328
8.1
18
8.1
296
7.8
18
8.0
304
Management
8.2
19
8.7
326
7.6
18
7.7
286
7.9
18
8.3
317
7.2
18
8.0
308
8.4
18
8.1
313
Generative AI
5.8
6
5.8
232
Agentic AI - Data Observability
Not enough data
6.3
28
Feature Not Available
6.4
28
Not enough data
6.8
28
Feature Not Available
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
Bigeye and Monte Carlo are categorized as DataOps Platforms and Data Observability
Unique Categories
Bigeye has no unique categories
Monte Carlo is categorized as Database Monitoring and Data Quality
Reviews
Reviewers' Company Size
Bigeye
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
22.7%
Computer Software
13.6%
Consulting
4.5%
Sports
4.5%
Online Media
4.5%
Other
50.0%
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
Bigeye 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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