Vector Database Comparison Cheat Sheet 2026

๐Ÿ“˜ Tutorials 2026-08-01 ยท Updated 2026-08-16 1 min read

Vector database cheat sheet 2026: 8 databases compared by stars, license and best use case.

💡 What You Will Learn

Vector database cheat sheet 2026: 8 databases compared by stars, license and best use case.

RAG is the standard AI architecture in 2026 and vector databases are its storage layer. Cheat sheet (GitHub stars snapshot mid-2026, verify live): Milvus 45K+ stars Apache-2.0, enterprise scale, billions of vectors. Qdrant 33K+ Apache-2.0, Rust production service, strong filters. Chroma 30K+ Apache-2.0, local prototyping. pgvector 22K+, PostgreSQL extension, easiest for existing Postgres. Typesense 26K+ GPL-3.0, keyword + vector hybrid search. Weaviate 16K+ BSD-3, graph + vector, out-of-box modules. Vespa 7K+ Apache-2.0, real-time ranking at huge scale. Elasticsearch for existing ES stacks. Choose: existing Postgres -> pgvector; serious product -> Qdrant or Milvus; prototyping -> Chroma; search products -> Typesense/ES. Retrieval quality mostly depends on the embedding model and chunking strategy, not the database itself.

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