Scaling Vector Search: From Prototype to Production

What happens when your Flat Index hits 1 million rows.

The O(N) Problem

A Flat Index compares your query vector to every single vector in the database.

• 10k rows: 5ms. (Fast)
• 1M rows: 500ms. (Slow)
• 100M rows: 50s. (Broken)

To scale, you need Approximate Nearest Neighbor (ANN) algorithms.

HNSW: The Gold Standard 🏆

Hierarchical Navigable Small World (HNSW) is the algorithm powering 90% of vector databases.

• How it works: It builds a multi-layered graph. You zoom in from high-level neighborhoods to specific streets.
• Trade-off: Fast search (milliseconds at scale), but SLOW insertion (building the graph takes time).
• Tuning: M (connections per node) and ef_construction (search depth). Higher numbers = better accuracy, slower build.

Sharding and Partitioning 🍰

When you exceed one machines RAM (vectors are heavy!), you must shard.

• Horizontal Sharding: Split the vectors across 5 nodes. Query all 5, merge results.
• Tenant Sharding: Give Company A their own shard. This guarantees that Company B data never leaks and search is fast for each tenant.

Product Quantization (PQ) 📉

To save RAM, we compress vectors.

• Original: 1536 floats (6KB per vector).
• Quantized: 1536 bytes (1.5KB per vector).
• Result: 4x density, 99% accuracy retention.

Conclusion

Scaling vector search is a memory management game. Use HNSW for speed, PQ for density, and Sharding for volume.

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*Next: [Hybrid Search](/blog/hybrid-search-vector-keyword)*

How This Article Was Made

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Stage	Who	What
Research	Claude Opus 4.5	Analyzed current industry data, studies, and expert sources
Curation	Tom Hundley	Directed focus, validated relevance, ensured strategic alignment
Drafting	Claude Opus 4.5	Synthesized research into structured narrative
Fact-Check	Human + AI	All statistics linked to original sources below
Editorial	Tom Hundley	Final review for accuracy, tone, and value

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