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Compare Bigeye and Monte Carlo 
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At a Glance
Market Segments
Small-Business (54.5% of reviews)
Enterprise (51.2% of reviews)
Entry-Level Pricing
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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
(462)4.4 out of 5
Market Segments
Enterprise (51.2% of reviews)
Pros & Cons
Entry-Level Pricing
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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 Bigeye follows closely with a score of 9.1. Reviewers mention that Monte Carlo's proactive alerts help in identifying data issues before they escalate, making it a strong choice for organizations prioritizing data integrity.
- Reviewers say that Monte Carlo's quality of support is outstanding, scoring 9.3, compared to Bigeye's 8.1. Users on G2 highlight that Monte Carlo's support team is responsive and knowledgeable, which significantly enhances the user experience.
- Users report that Monte Carlo offers superior monitoring capabilities with a score of 9.1, while Bigeye's monitoring features are rated lower. Reviewers mention that Monte Carlo's real-time alerts and anomaly identification tools are particularly effective in maintaining data observability.
- G2 users indicate that Bigeye shines in real-time analytics with a score of 8.3, surpassing Monte Carlo's score of 7.4. Users appreciate Bigeye's intuitive dashboard visualizations that make data insights easily accessible and actionable.
- Reviewers mention that Monte Carlo provides a more comprehensive end-to-end visibility experience, scoring 8.2, compared to Bigeye's 7.9. Users say that this feature is crucial for organizations needing to track data lineage and understand data flow across systems.
- Users on G2 report that Monte Carlo's automated workflows, scoring 7.9, are more robust than those offered by Bigeye. Reviewers highlight that this feature streamlines data management processes, allowing teams to focus on analysis rather than manual tasks.
Pricing
Entry-Level Pricing
Bigeye
Enterprise Starter
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- End-to-end lineage
- Cloud, on prem, and hybrid support
- Cross-source columnar mapping
Monte 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
Bigeye
No trial information availableMonte Carlo
No trial information availableRatings
Meets Requirements
8.0
22
8.3
430
Ease of Use
8.5
22
8.2
437
Ease of Setup
8.1
12
8.2
303
Ease of Admin
7.2
12
8.5
160
Quality of Support
8.2
20
9.0
386
Has the product been a good partner in doing business?
8.3
12
9.3
163
Product Direction (% positive)
7.8
21
8.9
426
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
8.3
5
8.5
49
8.3
5
8.5
45
8.7
5
8.6
49
9.3
5
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
9.0
5
7.8
48
9.7
5
7.7
46
Monitoring and Management
9.3
5
9.2
53
Not enough data
7.6
46
Cloud Deployment
9.0
5
7.4
42
9.0
5
7.0
40
Generative AI
Not enough data
6.2
33
Not enough data
6.1
33
Functionality
8.2
19
7.3
287
9.0
19
8.8
318
8.1
18
8.1
291
7.8
18
8.0
295
Management
8.2
19
8.7
313
7.6
18
7.8
284
7.9
18
8.3
307
7.2
18
8.0
301
8.4
18
8.1
307
Generative AI
5.8
6
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
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
14.2%
Information Technology and Services
10.9%
Computer Software
10.6%
Marketing and Advertising
3.8%
Manufacturing
3.5%
Other
57.0%
Alternatives
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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