Well, we have about 4B vectors we want to index, with a constantly changing dataset. We are actually running just fine on Elasticsearch with ~1B docs indexed right now, but the hardware costs are looking expensive.
Elasticsearch uses HNSW, not sure what options they have but quantization/compression will help reduce disk storage requirements. Alternatively, you can look at dimensionality reduction algorithms and only store that output in ES. Or pick a model with a small number of dimensions. For example https://huggingface.co/sentence-transformers/all-MiniLM-L6-v... only has 384 dims vs 768/1024/2048/4096.
