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Compare Neo4j Graph Data Science and Palantir Gotham 
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At a Glance
Market Segments
Mid-Market (46.7% of reviews)
Mid-Market (37.5% of reviews)
Entry-Level Pricing
Starting at $1.00 Per Month
No pricing available
Neo4j Graph Data Science
Star Rating
(16)4.5 out of 5
Market Segments
Mid-Market (46.7% of reviews)
Entry-Level Pricing
Starting at $1.00 Per Month
Browse all 3 pricing plansPalantir Gotham
Star Rating
(10)4.3 out of 5
Market Segments
Mid-Market (37.5% of reviews)
Entry-Level Pricing
No pricing available
Learn more about Palantir GothamSummary
AI Generated Summary
AI-generated. Powered by real user reviews.
- Users report that Palantir Gotham excels in data ingestion and wrangling with a score of 8.8, making it a strong choice for organizations needing to manage large datasets efficiently. In contrast, Neo4j Graph Data Science, while also robust, has a slightly lower score in this area, indicating it may not be as seamless for data preparation tasks.
- Reviewers mention that Palantir Gotham's quality of support is rated at 9.4, highlighting its strong customer service and responsiveness. On the other hand, Neo4j Graph Data Science, with a support rating of 8.8, may not provide the same level of assistance, which could be a consideration for users who prioritize support.
- G2 users indicate that Palantir Gotham shines in its ease of use with a score of 9.0, suggesting that users find it intuitive and user-friendly. Conversely, Neo4j Graph Data Science has a lower ease of use rating of 8.5, which may lead to a steeper learning curve for new users.
- Users on G2 report that Palantir Gotham offers superior model development capabilities, particularly with its pre-built algorithms scoring 9.0, which can significantly speed up the development process. Neo4j Graph Data Science, while strong in model training, scores slightly lower at 8.1, indicating it may require more manual effort in this area.
- Reviewers say that Palantir Gotham's data visualization features are rated at 8.3, providing users with effective tools to present their data insights. In comparison, Neo4j Graph Data Science has a slightly lower score of 8.1, which may affect users looking for advanced visualization options.
- Users report that Palantir Gotham's scalability is rated at 9.2, making it a preferred choice for organizations anticipating growth and needing to handle increasing data loads. Neo4j Graph Data Science, while still scalable with a score of 9.0, may not offer the same level of assurance for rapidly expanding businesses.
Pricing
Entry-Level Pricing
Neo4j Graph Data Science
AuraDS Professional
Starting at $1.00
Per Month
AuraDS Professional is the fastest way to take your graph data science experiments to production with monthly billing.
- 8-96GB (RAM)
- Pay as you go: credit card or Google Cloudbilling
- Automated updates/patches
Palantir Gotham
No pricing availableFree Trial
Neo4j Graph Data Science
No trial information availablePalantir Gotham
No trial information availableRatings
Meets Requirements
9.0
13
9.4
6
Ease of Use
8.5
13
9.0
5
Ease of Setup
7.9
8
Not enough data
Ease of Admin
8.3
8
Not enough data
Quality of Support
8.8
13
9.4
6
Has the product been a good partner in doing business?
9.4
8
Not enough data
Product Direction (% positive)
9.1
13
10.0
7
Features by Category
System
8.8
10
Not enough data
Model Development
7.9
11
Not enough data
8.1
8
Not enough data
8.5
10
Not enough data
8.5
9
Not enough data
Model Development
8.8
7
Not enough data
Machine/Deep Learning Services
Feature Not Available
Not enough data
8.0
5
Not enough data
Feature Not Available
Not enough data
8.0
5
Not enough data
Machine/Deep Learning Services
Feature Not Available
Not enough data
Not enough data
Not enough data
Deployment
7.9
7
Not enough data
8.8
10
Not enough data
9.2
8
Not enough data
Generative AI
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Agentic AI - Data Science and Machine Learning Platforms
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
Not enough data
Not enough data
Not enough data
Not enough data
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
Analytics
Not enough data
Not enough data
Security
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 - Data Fabric
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
Development
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
Not enough data
Not enough data
Not enough data
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
Not enough data
Not enough data
Security
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Integration Options
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Deployment
Not enough data
Not enough data
Not enough data
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
Integration - Machine Learning
Not enough data
Not enough data
Learning - Machine Learning
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Statistical Tool
8.9
6
Not enough data
8.6
6
Not enough data
9.2
6
Not enough data
Data Analysis
9.4
6
Not enough data
9.3
5
Not enough data
Decision Making
9.2
6
Not enough data
8.3
5
Not enough data
8.1
6
Not enough data
Not enough data
Not enough data
Generative AI
Not enough data
Not enough data
Not enough data
Not enough data
Categories
Categories
Shared Categories
Neo4j Graph Data Science and Palantir Gotham are categorized as Data Science and Machine Learning Platforms and Predictive Analytics
Unique Categories
Neo4j Graph Data Science is categorized as Machine Learning
Palantir Gotham is categorized as Master Data Management (MDM), Data Fabric, Big Data Integration Platforms, and iPaaS
Reviews
Reviewers' Company Size
Neo4j Graph Data Science
Small-Business(50 or fewer emp.)
Mid-Market(51-1000 emp.)
Enterprise(> 1000 emp.)
Palantir Gotham
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
Computer Software
26.7%
Marketing and Advertising
13.3%
Information Technology and Services
13.3%
Biotechnology
13.3%
Sports
6.7%
Other
26.7%
Computer Software
37.5%
Transportation/Trucking/Railroad
12.5%
Information Technology and Services
12.5%
Government Relations
12.5%
Airlines/Aviation
12.5%
Other
12.5%
Alternatives
Discussions
Neo4j Graph Data Science has no discussions with answers
Palantir Gotham has no discussions with answers
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