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Compare Monte Carlo and Sifflet 
At a Glance
Market Segments
Enterprise (50.7% of reviews)
Mid-Market (65.9% of reviews)
Entry-Level Pricing
Contact Us
Contact Us 1001 Tables Per Year
Monte 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 plansSifflet
Star Rating
(47)4.4 out of 5
Market Segments
Mid-Market (65.9% of reviews)
Pros & Cons
Entry-Level Pricing
Contact Us 1001 Tables Per Year
Browse all 3 pricing plansSummary
AI Generated Summary
AI-generated. Powered by real user reviews.
- G2 reviewers report that Monte Carlo excels in providing a comprehensive view of data quality issues, with users highlighting the ability to view model lineage, tests, and alerts all in one application. This feature is deemed invaluable for data teams, enhancing their operational efficiency.
- Users say Sifflet stands out for its predictive capabilities, learning from past data trends to identify anomalies. Reviewers appreciate that it only alerts them when data behaves differently than expected, which helps reduce noise and focus on significant issues.
- According to verified reviews, Monte Carlo's user interface is praised for its intuitiveness, making it easier for teams to track and resolve data issues. Users have noted that the real-time alerts significantly improve their awareness of ongoing data problems, allowing for quicker resolutions.
- Reviewers mention that Sifflet has been instrumental in moving teams away from constant firefighting. Users report that it helps them catch data issues early, which streamlines their processes and enhances overall data pipeline management.
- G2 reviewers highlight that while Monte Carlo has a higher overall satisfaction score, Sifflet's quality of support is slightly better, with users noting that Sifflet provides clear ownership across data pipelines, which aids in accountability and faster issue resolution.
- Users report that both products have their strengths, but Monte Carlo's higher G2 Score reflects a broader user base and satisfaction, while Sifflet's focus on mid-market needs allows it to cater effectively to smaller teams looking for robust data observability solutions.
Pricing
Entry-Level Pricing
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
Sifflet
Enterprise
Contact Us
1001 Tables Per Year
Great for large, regulated or complex organizations
- Core Data Observability & Catalog (Fundamental metrics: freshness, schema, volume, custom metrics...)
- Business-Aware Lineage & Impact Analysis
- Automated Root-Cause Analysis
Free Trial
Monte Carlo
No trial information availableSifflet
Free Trial is available
Ratings
Meets Requirements
8.3
445
8.5
39
Ease of Use
8.2
452
8.6
39
Ease of Setup
8.1
317
8.5
28
Ease of Admin
8.5
161
8.7
15
Quality of Support
9.0
399
9.0
32
Has the product been a good partner in doing business?
9.2
164
8.6
15
Product Direction (% positive)
8.9
442
8.5
38
Features by Category
Data Governance
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Data Preparation
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Collaboration
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Artificial Intelligence
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Generative AI
Not enough data
Not enough data
Not enough data
Not enough data
Agentic AI - Machine Learning Data Catalog
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Functionality
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
Not enough data
Agentic AI - Database Monitoring
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
Not enough data
Management
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Functionality
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Security
Not enough data
Not enough data
Not enough data
Not enough data
Data Management
8.6
52
Not enough data
8.5
48
Not enough data
8.6
52
Not enough data
7.9
50
Not enough data
Agentic AI - DataOps Platforms
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
Not enough data
Analytics
7.9
51
Not enough data
7.7
48
Not enough data
Monitoring and Management
9.2
56
Not enough data
7.7
49
Not enough data
Cloud Deployment
7.5
44
Not enough data
7.1
42
Not enough data
Generative AI
6.3
35
Not enough data
6.2
35
Not enough data
Functionality
7.4
292
8.1
30
8.8
328
9.0
36
8.1
296
8.4
33
8.0
304
8.0
35
Management
8.7
326
8.5
35
7.7
286
8.1
32
8.3
317
8.7
31
8.0
308
8.5
33
8.1
313
7.8
32
Generative AI
5.8
232
7.0
20
Agentic AI - Data Observability
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
Not enough data
Functionality
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
Not enough data
Management
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
Not enough data
Generative AI
5.2
145
Not enough data
5.3
145
Not enough data
Categories
Categories
Shared Categories
Monte Carlo and Sifflet are categorized as DataOps Platforms, Database Monitoring, Data Observability, and Data Quality
Unique Categories
Monte Carlo has no unique categories
Sifflet is categorized as Master Data Management (MDM) and Machine Learning Data Catalog
Reviews
Reviewers' Company Size
Monte Carlo
Small-Business(50 or fewer emp.)
Mid-Market(51-1000 emp.)
Enterprise(> 1000 emp.)
Sifflet
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
13.9%
Computer Software
11.3%
Information Technology and Services
11.1%
Marketing and Advertising
3.6%
Manufacturing
3.4%
Other
56.5%
Information Technology and Services
14.6%
Computer Software
12.2%
Retail
9.8%
Pharmaceuticals
7.3%
Financial Services
7.3%
Other
48.8%
Discussions
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
Sifflet has no discussions with answers
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