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Choosing a vector database for scale (EXPLAINED)

Company BasedVector DatabasesHard25 min read

500M vectors at sub-100ms p99 is a staff-level vector search design question from Uber, Airbnb, and large-scale ML platform teams. Learn sharding strategies, index tuning, and the operational trade-offs that separate senior from principal engineers.

Choosing a vector database for scale
Vector Databases · System Design

TL;DR — Quick Answer

Sharded vector index, embedding cache, tiered storage, dedicated ANN service, and aggressive index tuning with monitoring.

The Interview Question

You need to store 500M vectors with <100ms p99 latency. How do you architect the vector search layer?

Deep Explanation

At 500M scale: partition by tenant/use-case, use managed vector DB with auto-scaling (Pinecone, Milvus) or self-hosted Milvus/Qdrant cluster. Optimize: HNSW ef_search params, product quantization for memory, pre-filtering with metadata, query embedding cache, and read replicas for query load.

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