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Design a Vector Database is a hard system design interview question covering Distributed Systems, vector search, machine learning, ANN — the kind asked at FAANG (Facebook/Meta, Apple, Amazon, Netflix, Google) and AI-first companies like Anthropic, OpenAI, and Nvidia.
Design a vector database for 10 billion 1024-dimensional embeddings with 100 million new vectors arriving daily. Queries run at 50,000 per second with recall@10 of at least 95% under 50ms p99, nearly always combined with metadata filters, and new vectors must be searchable within a minute.
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