ApertureDB

Embedded multimodal vector database on the Lance format.

An open-source retrieval engine for AI built on the Lance columnar format. It runs in-process alongside your app — no separate server — and stores, indexes, and searches vectors, metadata, and multimodal data (text, images, video) with vector, full-text, and SQL queries. A managed enterprise lakehouse tier scales the same engine to petabytes.

Open-source vector database with built-in vectorisers.

Cloud-native vector DB that can compute embeddings inline — pass raw text in, store vectors out. Strong hybrid (BM25 + vector) search; self-hostable or managed.

Distributed open-source vector DB built for billion-scale.

Cloud-native, Apache-2.0 vector database for similarity search at scale, powering RAG, semantic and multimodal search, and recommendations. Its distributed architecture separates storage and compute and supports many index types (HNSW, IVF, FLAT, DiskANN, SCANN) with quantization and mmap. Created by Zilliz, which offers the managed Zilliz Cloud.

Open-source, Rust-based vector DB. Fast, predictable, self-hostable.

Vector database written in Rust with a strong focus on filtering, payloads, and predictable latency at scale. Self-host on a single binary or use the managed cloud.