#quantization
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How to Choose a Vector Database: Fix the Recall Target, Measure on Your Data, Then Shop
How to choose a vector database for production retrieval: the five constraints that decide it, the one metric to benchmark on your own corpus, a Python harness to measure it, and where pgvector, Qdrant, Milvus, Weaviate, Pinecone and Elasticsearch actually differ.
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Vector Database Memory Sizing: RAM, Graph and Overhead
Size a vector index before you build it: bytes per embedding, HNSW graph overhead, what quantization actually saves, and what has to fit in RAM.
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Vector Search Fundamentals: Embeddings, ANN and Recall
What an approximate nearest neighbor index does, how graph and cluster based indexes differ, and how quantization trades memory against recall.