Fine-tuning an embedding model is unusually cheap — a base model of a hundred million parameters, fifty thousand pairs and an hour on one GPU. What is not cheap is everything that happens after: a fine-tuned model is a new model, and every vector you have stored becomes incompatible on the day you deploy it. When this is worth doing Fine-tuning pays when your domain’s notion of relevance differs from the general one the base model learned. Concretely: your users search with vocab...