Chroma vs turbovec
Open-source embedding database for building AI apps with retrieval. — versus — Rust vector index with Python bindings built on Google's TurboQuant: no training step, online ingest, hand-written SIMD kernels — 10M x 1536 vectors in ~4 GB, with allowlist-filtered search.
Both give you dense retrieval in-process. Chroma is a batteries-included embedding database with metadata, collections and persistence; turbovec is only the index — dramatically smaller and faster, but you supply everything around it.
| Chroma | turbovec | |
|---|---|---|
| Stars | 29k | 17k |
| Forks | 2.5k | 1.4k |
| Language | Python | Rust |
| License | Apache-2.0 | MIT |
| Last activity | yesterday | 10 days ago |
| Topics | rag, memory | rag, local, storage |
| Curated connections | 15 | 5 |
Chroma — the curator's take
Open-source embedding database for building AI apps with retrieval.
turbovec — the curator's take
Use it when the vector index is a component, not a product: you already have Postgres or BM25 doing the filtering and just need dense rerank that fits in RAM and accepts writes without a rebuild. The data-oblivious quantizer is the real ergonomic win — no train step, no parameter tuning, no reindex as the corpus grows, which is exactly the FAISS ritual people hate. It is an index, not a database: no metadata, no replication, no server, no hybrid scoring, bring your own storage and ID mapping. And it is quantized, so measure recall on your own corpus before you trust it over exact search.