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Compare Monte Carlo and Sifflet 
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At a Glance
Market Segments
Enterprise (51.2% of reviews)
Mid-Market (68.9% of reviews)
Entry-Level Pricing
Contact Us
Contact Us 1001 Tables Per Year
Monte Carlo
Star Rating
(462)4.4 out of 5
Market Segments
Enterprise (51.2% of reviews)
Pros & Cons
Entry-Level Pricing
Contact Us
Browse all 3 pricing plansSifflet
Star Rating
(45)4.6 out of 5
Market Segments
Mid-Market (68.9% of reviews)
Pros & Cons
Entry-Level Pricing
Contact Us 1001 Tables Per Year
Browse all 3 pricing plansMonte Carlo vs Sifflet
When assessing the two solutions, reviewers found Sifflet easier to use, set up, and administer. However, reviewers felt that both vendors make it equally easy to do business overall.
- Reviewers felt that Sifflet meets the needs of their business better than Monte Carlo.
- When comparing quality of ongoing product support, reviewers felt that Sifflet is the preferred option.
- When assessing product direction, Monte Carlo and Sifflet received similar ratings from our reviewers.
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
430
8.8
43
Ease of Use
8.2
437
8.8
43
Ease of Setup
8.2
303
8.9
32
Ease of Admin
8.5
160
9.1
20
Quality of Support
9.0
386
9.4
37
Has the product been a good partner in doing business?
9.3
163
9.3
20
Product Direction (% positive)
8.9
426
8.9
43
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
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
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
8.5
49
Not enough data
8.5
45
Not enough data
8.6
49
Not enough data
7.9
47
Not enough data
Agentic AI - DataOps Platforms
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
Not enough data
Analytics
7.8
48
Not enough data
7.7
46
Not enough data
Monitoring and Management
9.2
53
Not enough data
7.6
46
Not enough data
Cloud Deployment
7.4
42
Not enough data
7.0
40
Not enough data
Generative AI
6.2
33
Not enough data
6.1
33
Not enough data
Functionality
7.3
287
8.3
36
8.8
318
9.0
41
8.1
291
8.6
40
8.0
295
8.5
41
Management
8.7
313
8.8
40
7.8
284
8.3
39
8.3
307
8.9
37
8.0
301
8.7
39
8.1
307
8.0
39
Generative AI
5.8
227
7.6
26
Agentic AI - Data Observability
6.1
27
9.6
9
6.2
27
10.0
9
6.7
27
9.8
9
6.4
26
9.6
9
6.7
29
9.6
9
Functionality
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
Not enough data
Management
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
Not enough data
Generative AI
5.2
142
Not enough data
5.3
142
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
14.2%
Information Technology and Services
10.9%
Computer Software
10.6%
Marketing and Advertising
3.8%
Manufacturing
3.5%
Other
57.0%
Information Technology and Services
22.2%
Computer Software
13.3%
Retail
8.9%
Financial Services
6.7%
Restaurants
4.4%
Other
44.4%
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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