Demystify the vector database ecosystem: explore in-memory libraries vs dedicated engines, pgvector tradeoffs, managed vector platforms, hybrid search algorithms, and exact memory sizing math.
- 1 Do You Need a Vector Database or Just a Library? Discover whether your project actually requires a dedicated vector database or if an embedded in-memory library like FAISS, USearch, or sqlite-vec provides faster, simpler search with zero infrastructure overhead. Scheduled · December 9, 2026
- 2 pgvector and Use the Database You Have: Pragmatic Vector Search Learn why extending PostgreSQL with pgvector is the most pragmatic vector search architecture for production apps, eliminating dual-write sync bugs while delivering sub-10ms HNSW query speeds. Scheduled · December 10, 2026
- 3 The Managed Vector Stores Landscape: Pinecone, Weaviate, Qdrant, Chroma, and Milvus Navigate the dedicated vector database ecosystem: compare Pinecone, Qdrant, Weaviate, Milvus, and Chroma across memory models, serverless pricing, and self-hosted operational trade-offs. Scheduled · December 11, 2026
- 4 Hybrid Search and Metadata Filtering: The Secret to Production Retrieval Discover why pure vector search fails on product SKUs and exact keywords, and how combining BM25 lexical matching with dense vectors via Reciprocal Rank Fusion unlocks production retrieval accuracy. Scheduled · December 12, 2026
- 5 Vector Database Cost, Sizing, and Infrastructure for Beginners Master vector capacity planning: learn how embedding dimensions, HNSW graph multipliers, and scalar quantization determine your RAM footprint and cloud hosting bill. Scheduled · December 13, 2026
- 6 Choosing the Right Vector Stack: Personal Side Project vs Enterprise Production Synthesize your vector architecture: explore the definitive decision framework for choosing between embedded libraries, relational pgvector, and distributed cloud clusters from prototype to global scale. Scheduled · December 14, 2026