txtai

Embedded vector DB. Pip-install, prototype, scale later.

The low-friction starting point — Chroma runs embedded inside your Python process or as a hosted service. Great for prototypes and small-to-medium RAG apps; upgrade to a managed option when you outgrow it.

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.

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.