Building a Production-Grade Vector Database in Rust: What We Shipped

A deep-dive into the latest FerresDB updates — from HNSW auto-tuning and PolarQuant compression to Point-in-Time Recovery, cross-encoder reranking, and a distributed Raft foundation. Over the past few months, FerresDB has grown from a focused vector search PoC into something that increasingly resembles a production system. This post walks through everything we've shipped recently — the architectural decisions, the tradeoffs, and the honest "here's why we did it this way" behind each feature. If you're building RAG pipelines, recommendation systems, or any kind of semantic search on top of Rust, this is the kind of update post you'd want to read before picking your stack. The Baseline: What FerresDB Already Had Before diving into the new stuff, a quick recap of the foundation: HNSW index with Cosine, Euclidean, and Dot Product metrics WAL (Write-Ahead Log) with periodic snapshots and crash recovery Hybrid search combining vector similarity with BM25 full-text scoring Scalar Quantization