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 ...

100%That's how much data you re-index when you change embedding models.And most teams treat that as unavoidable.Imagine you built a RAG pipeline using an embedding model with high retrieval quality, and it ships to production.Six months later, a better embedding model is released that delivers similar quality at a lower cost.But your existing embeddings live in one vector space, while the new model produces embeddings in a different one, which makes them incompatible.Switching models now means rebuilding th ...