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At a Glance
Market Segments
Small-Business (40.5% of reviews)
Mid-Market (46.7% of reviews)
Entry-Level Pricing
No pricing available
Starting at $1.00 Per Month
IBM watsonx.ai
Star Rating
(122)4.4 out of 5
Market Segments
Small-Business (40.5% of reviews)
Entry-Level Pricing
No pricing available
Free Trial is available
Learn more about IBM watsonx.aiNeo4j 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 plansSummary
AI Generated Summary
AI-generated. Powered by real user reviews.
- Users report that Neo4j Graph Data Science excels in data ingestion and wrangling with a score of 8.8, allowing for efficient handling of complex graph data, while IBM watsonx.ai, with a score of 8.2, is noted for its slightly less robust capabilities in this area.
- Reviewers mention that Neo4j's scalability is impressive, scoring 9.0, which is crucial for handling large datasets, whereas IBM watsonx.ai, although still strong at 8.5, may not scale as effectively for extensive graph-based applications.
- G2 users highlight that Neo4j's model registry feature scores a remarkable 9.3, providing excellent management of machine learning models, while IBM watsonx.ai's model management features, although solid, score slightly lower at 8.3, indicating room for improvement.
- Users on G2 report that Neo4j's pre-built algorithms are highly effective, scoring 9.0, which enhances user experience by simplifying model development, while IBM watsonx.ai's pre-built algorithms score 8.5, suggesting that users may need to invest more time in customization.
- Reviewers mention that Neo4j's ease of setup is rated at 7.9, which some users find challenging compared to IBM watsonx.ai's higher score of 9.1, indicating a more user-friendly onboarding process for the latter.
- Users say that Neo4j's quality of support is rated at 8.8, which is on par with IBM watsonx.ai, but the latter's support is noted for being slightly more responsive, also scoring 8.8, making it a reliable choice for users needing assistance.
Pricing
Entry-Level Pricing
IBM watsonx.ai
No pricing availableNeo4j 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
Free Trial
IBM watsonx.ai
Free Trial is available
Neo4j Graph Data Science
No trial information availableRatings
Meets Requirements
8.8
77
9.0
13
Ease of Use
8.9
109
8.5
13
Ease of Setup
8.5
100
7.9
8
Ease of Admin
8.7
36
8.3
8
Quality of Support
8.8
76
8.8
13
Has the product been a good partner in doing business?
8.9
36
9.4
8
Product Direction (% positive)
9.9
79
9.1
13
Features by Category
Deployment
9.1
9
Not enough data
8.5
9
Not enough data
7.8
9
Not enough data
8.7
9
Not enough data
8.7
9
Not enough data
Deployment
9.3
9
Not enough data
8.7
9
Not enough data
8.3
9
Not enough data
8.9
9
Not enough data
9.1
9
Not enough data
Management
8.0
9
Not enough data
8.5
9
Not enough data
8.5
9
Not enough data
9.3
9
Not enough data
Operations
9.1
9
Not enough data
8.7
9
Not enough data
9.3
9
Not enough data
Management
8.5
9
Not enough data
9.0
8
Not enough data
8.5
8
Not enough data
Generative AI
9.1
9
Not enough data
9.3
9
Not enough data
System
8.2
31
8.8
10
Model Development
8.6
32
7.9
11
8.2
32
8.1
8
8.7
31
8.5
10
8.4
32
8.5
9
Model Development
8.5
32
8.8
7
Machine/Deep Learning Services
Feature Not Available
Feature Not Available
8.9
32
8.0
5
8.6
32
Feature Not Available
8.1
32
8.0
5
Machine/Deep Learning Services
8.5
32
Feature Not Available
8.8
32
Not enough data
Deployment
8.2
32
7.9
7
8.6
32
8.8
10
8.8
32
9.2
8
Generative AI
8.8
31
Not enough data
8.8
31
Not enough data
Feature Not Available
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 Type
8.8
13
Not enough data
Feature Not Available
Not enough data
8.5
12
Not enough data
Synthesis Type
9.0
12
Not enough data
9.2
12
Not enough data
Data Transformation
8.6
12
Not enough data
9.3
12
Not enough data
9.7
12
Not enough data
9.2
12
Not enough data
9.2
12
Not enough data
Scalability and Performance - Generative AI Infrastructure
9.3
7
Not enough data
8.8
7
Not enough data
9.3
7
Not enough data
Cost and Efficiency - Generative AI Infrastructure
8.3
7
Not enough data
8.6
7
Not enough data
8.3
7
Not enough data
Integration and Extensibility - Generative AI Infrastructure
9.5
7
Not enough data
8.6
7
Not enough data
8.8
7
Not enough data
Security and Compliance - Generative AI Infrastructure
8.3
7
Not enough data
8.8
7
Not enough data
8.6
7
Not enough data
Usability and Support - Generative AI Infrastructure
9.3
7
Not enough data
9.0
7
Not enough data
Content Generation - AI Content Creation Platforms
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Management - AI Content Creation Platforms
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Integration - Machine Learning
9.0
21
Not enough data
Learning - Machine Learning
9.2
22
Not enough data
9.1
22
Not enough data
9.0
21
Not enough data
Prompt Engineering - Large Language Model Operationalization (LLMOps)
9.2
6
Not enough data
8.1
6
Not enough data
Inference Optimization - Large Language Model Operationalization (LLMOps)
8.9
6
Not enough data
Model Garden - Large Language Model Operationalization (LLMOps)
8.9
6
Not enough data
Custom Training - Large Language Model Operationalization (LLMOps)
8.1
6
Not enough data
Application Development - Large Language Model Operationalization (LLMOps)
8.3
6
Not enough data
Model Deployment - Large Language Model Operationalization (LLMOps)
8.3
6
Not enough data
8.6
6
Not enough data
Guardrails - Large Language Model Operationalization (LLMOps)
9.4
6
Not enough data
8.6
6
Not enough data
Model Monitoring - Large Language Model Operationalization (LLMOps)
8.6
6
Not enough data
8.9
6
Not enough data
Security - Large Language Model Operationalization (LLMOps)
9.4
6
Not enough data
9.2
6
Not enough data
Gateways & Routers - Large Language Model Operationalization (LLMOps)
8.9
6
Not enough data
Customization - AI Agent Builders
8.8
7
Not enough data
9.0
7
Not enough data
9.0
7
Not enough data
Functionality - AI Agent Builders
8.6
7
Not enough data
9.0
7
Not enough data
9.3
7
Not enough data
8.8
7
Not enough data
Data and Analytics - AI Agent Builders
9.0
7
Not enough data
8.8
7
Not enough data
9.0
7
Not enough data
Integration - AI Agent Builders
9.0
7
Not enough data
9.0
7
Not enough data
9.0
7
Not enough data
8.6
7
Not enough data
Data Ingestion & Preparation - Low-Code Machine Learning Platforms
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Model Construction & Automation - Low-Code Machine Learning Platforms
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Statistical Tool
Not enough data
8.9
6
Not enough data
8.6
6
Not enough data
9.2
6
Data Analysis
Not enough data
9.4
6
Not enough data
9.3
5
Decision Making
Not enough data
9.2
6
Not enough data
8.3
5
Not enough data
8.1
6
Not enough data
Not enough data
Generative AI
Not enough data
Not enough data
Not enough data
Not enough data
Categories
Categories
Shared Categories
IBM watsonx.ai and Neo4j Graph Data Science are categorized as Data Science and Machine Learning Platforms and Machine Learning
Unique Categories
IBM watsonx.ai is categorized as Synthetic Data, MLOps Platforms, AI Content Creation Platforms, Generative AI Infrastructure, Large Language Model Operationalization (LLMOps), AI Agent Builders, and Low-Code Machine Learning Platforms
Neo4j Graph Data Science is categorized as Predictive Analytics
Reviews
Reviewers' Company Size
IBM watsonx.ai
Small-Business(50 or fewer emp.)
Mid-Market(51-1000 emp.)
Enterprise(> 1000 emp.)
Neo4j Graph Data Science
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
18.9%
Computer Software
11.7%
Consulting
7.2%
Banking
6.3%
Marketing and Advertising
5.4%
Other
50.5%
Computer Software
26.7%
Marketing and Advertising
13.3%
Information Technology and Services
13.3%
Biotechnology
13.3%
Sports
6.7%
Other
26.7%
Alternatives
Discussions
IBM watsonx.ai has no discussions with answers
Neo4j Graph Data Science has no discussions with answers
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