I replaced my 500MB vector database Docker stack with a 3MB embedded engine

Most vector database tutorials start the same way: docker pull qdrant/qdrant docker run -p 6333:6333 qdrant/qdrant That's 500MB+ of Docker image, a running server process, a REST API to talk to, and a container to babysit in production. For what? Storing a few thousand embeddings and doing similarity search. I've been building AI features for a project where everything runs locally: no cloud, no Docker, no external dependencies. I needed a vector store that I could pip install and forget about. So I built VelesDB , an embedded database written in Rust. Here's what it looks like in practice. Setup: one line pip install velesdb That's it. No Docker. No config files. No server to start. The entire engine is a ~3MB native binary that ships inside the Python wheel. Create a database and index documents import velesdb from sentence_transformers import SentenceTransformer model = SentenceTransformer ( " all-MiniLM-L6-v2 " ) # 384 dimensions db = velesdb . Database ( " ./my_vectors " ) collect