Strengths and tradeoffs
What each tool does well, and the limitations to keep in mind.
Pros
- Unified platform combining transactional, analytical, and vector workloads
- Co-located vector data with relational data for powerful SQL queries
- Multiple vector index types (FLAT, IVF_FLAT, IVF_PQ, HNSW_FLAT, HNSW_PQ)
- MySQL and MongoDB wire protocol compatibility for easy integration
- Proven at scale with 100+ Fortune 500 customers
Cons
- More complex than dedicated vector databases for pure vector search use cases
- Pricing not transparently published
- May have higher learning curve for teams unfamiliar with distributed SQL
- Recent acquisition may lead to product changes
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