Strengths and tradeoffs
What each tool does well, and the limitations to keep in mind.
Pros
- Most widely deployed open-source vector database with massive community
- Mature ELK stack ecosystem with extensive integrations and tools
- DiskBBQ algorithm enables efficient vector search without full in-memory indexes
- Powerful hybrid search combining keyword, filters, and vector similarity
- Consumption-based pricing—pay only for what you use
Cons
- Can be complex to configure and optimize for vector search
- Not purpose-built for vector search—added as a feature
- Resource-intensive at scale without proper optimization
- Pricing model complexity with multiple tiers and factors
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