Vector Database HNSW Memory Footprint.

## The Expensive Reality of Vector Databases

Vector databases like Pinecone, Milvus, and Qdrant provide lightning-fast semantic similarity search (Cosine Similarity). However, to achieve single-digit millisecond latency across millions of embeddings, the index absolutely MUST be held entirely in your system's Random Access Memory (RAM).

### FAQ

**Q: What is the HNSW algorithm?**
A: Hierarchical Navigable Small World (HNSW) is the standard indexing algorithm for vector search. It builds a multi-layered graph of your embeddings, allowing a search query to rapidly "skip" across the map to locate the nearest neighbors without scanning the entire database.

**Q: How do I reduce memory costs?**
A: Utilize Product Quantization (PQ) to compress vectors, drop your embeddings from 32-bit floats to 8-bit integers (Int8 Quantization), or use an embedding model with fewer dimensions if complex reasoning isn't strictly required.