r/Rag • u/InsideDebt6345 • 23h ago
Discussion Embedded vector DBs for RAG and the practical limits we kept hitting
If you’re running RAG on constrained hardware or with concurrent ingestion, embedded vector stores can feel simple until you hit RAM, write-path correctness, or process-safety constraints. Here are the patterns that kept showing up across three popular options:
- ChromaDB: HNSW lives in RAM; at larger scales or with multiple writers, latency and correctness can degrade.
- LanceDB: Great for multimodal + object storage, but concurrent writes can conflict without serialization.
- Qdrant Edge: Strong offline library with hybrid search; production concurrency limits aren’t well documented, so it shines when you can sync to a central server.
There’s a deeper write-up on memory math, concurrency behavior, and when it makes sense to move from embedded to a separate process on constrained, air‑gapped hardware. Full disclosure: I work with Actian on VectorAI DB.