There are good reasons to split embeddings off from your regular DB load. Embeddings can be quite large (kilobytes per item), and querying is CPU and IO intensi

There are good reasons to split embeddings off from your regular DB load. Embeddings can be quite large (kilobytes per item), and querying is CPU and IO intensive, so they are good candidate for scaling separately. The embeddings are not primary data, they're derived from other content and can be recreated if necessary. The vectors will probably be fed or read in bulk by your ML pipeline at times. I'd say you probably want the primary storage of your embeddings to be file storage like S3 or similar, and then feed them directly into a search engine like Vespa or Elasticsearch.