As query traffic grows, so does the compute bill for embedding it. On a low-power device, a large model may not fit in memory. A smaller query model could reduce that cost, but switching usually means re-embedding the collection.

Constella lets you make that switch. It’s a family of models on Hugging Face built around Stella, a 400M-parameter English embedding model. Stella encodes your documents. Zero, Nano, or Stella itself can then encode your queries, all searching the same Qdrant collection.