RT Avi Chawla (@_avichawla)Your embedding stack forces a 100% re-index just to change models.And most teams treat that as unavoidable.Imagine you built a RAG pipeline with a large embedding model for high retrieval quality, and it ships to production.Six months later, your application traffic and your embedding model costs are soaring while your pipeline struggles to scale. You want to switch to a model that prioritizes cost and latency in order to meet this new demand.But your existing embeddings live in o ...

Your embedding stack forces a 100% re-index just to change models.And most teams treat that as unavoidable.Imagine you built a RAG pipeline with a large embedding model for high retrieval quality, and it ships to production.Six months later, your application traffic and your embedding model costs are soaring while your pipeline struggles to scale. You want to switch to a model that prioritizes cost and latency in order to meet this new demand.But your existing embeddings live in one vector space, while the ...