Vector Databases in Production: Lessons Learned

After deploying vector search across multiple production systems, here are the hard-won insights that documentation doesn't cover.

Indexing Strategies Matter

The choice between HNSW, IVF, and flat indexes isn't just about speed—it's about trade-offs:

// HNSW: Fast queries, slow inserts, high memory
const hnswConfig = {
  m: 16,           // Connections per node
  efConstruction: 200,  // Build-time accuracy
  efSearch: 100,   // Query-time accuracy
};

// IVF: Balanced, good for large datasets
const ivfConfig = {
  nlist: 1000,     // Number of clusters
  nprobe: 10,      // Clusters to search
};

Query Optimization

The biggest performance gains come from reducing the search space before vector comparison.

Pre-filtering with metadata can reduce query time by 10x.