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
- Up to 100x cost reduction compared to traditional vector databases
- Sub-10ms p50 latency with serverless auto-scaling
- No namespace limits unlike competitors
- Enterprise compliance (HIPAA, SOC 2, CMEK) on standard plan
- Trusted by major companies: Notion, Cursor, Linear, PlayerZero
Cons