Selecting the Right Vector Index§
Choosing the appropriate vector indexing algorithm is the single most critical decision when architecting RAG applications and vector search platforms. The trade-off between Recall Accuracy, Queries Per Second (QPS), RAM Footprint, and Index Build Time dictates infrastructure costs at scale.
---
Index Architecture Comparison§
| Index Type | Search Speed (QPS) | Recall @ 10 | RAM Footprint | Build Time | Best Use Case |
|---|---|---|---|---|---|
| HNSW | Ultra Fast (1,200 QPS) | 99.2% | High (In-Memory) | Moderate | Production RAG (<10M vectors) |
| IVFFlat | Moderate (450 QPS) | 91.5% | Low | Fast | Rapid prototyping / batch search |
| DiskANN | High (850 QPS) | 97.8% | Ultra Low (SSD) | Slow | Billion-scale datasets |
---
Recommendation for Multi-Tenant Architecture§
For multi-tenant platforms hosting isolated vector namespaces per user, HNSW indexes in pgvector or Qdrant allow combining tenant ID scalar filtering with high-speed vector retrieval in a single database query.
