Strengths and tradeoffs
What each tool does well, and the limitations to keep in mind.
Pros
- Industry-first distributed native graph database with vector capabilities
- Powerful for interconnected data analytics and machine learning
- Flexible pricing with granular measurement (1-minute for compute)
- Comprehensive use cases: fraud detection, recommendations, cybersecurity, knowledge graphs
- Graph + Vector DB on single server simplifies architecture
Cons
- Steeper learning curve for teams unfamiliar with graph databases
- May be over-engineered for simple vector search use cases
- Smaller community compared to more popular vector databases
- Pricing details require consultation for accurate estimates
Pros
- Scales to billions of data items with sub-100ms query latencies
- True hybrid search combining vector, text, and structured filters in single query
- Native tensor support for advanced machine learning ranking
- Real-time updates and inference at massive scale
- Open-source foundation with enterprise support
Cons