If you look at a embeddings leaderboard [1], one of the top competitors called InstructorXL [2] is just a pip install away. It's neck and neck with Ada v2 except for a shorter input length and half the dimensions, with the added benefit that you'll always have the model available.

Most of the other options just work with the transformers library.

[1] https://huggingface.co/spaces/mteb/leaderboard

[2] https://github.com/HKUNLP/instructor-embedding

Running models on your own hardware isn't just about cost, there are privacy concerns too.

If you've never coded or used Python before, yeah, go with OpenAI. Otherwise, generating embeddings with SentenceBERT takes 5 minutes.

And from my personal experience Ada embeddings are not the best. They are large (makes aproximate searching harder), are distributed weirdly, and zimply put, other embeddings give better results for retrieval.

Another advantage is that you are not an OA's whim: they just announced the deprecation of some previous model. What are you going to do when they will deprecate Ada v2 and you've built a huge system on top of it? You'll have to regenerate embeddings and hope everything still works just as well.

Yes, exactly this, I also want to say I'm not someone who generally thinks open models are better. I think embeddings just haven't been a focus for OpenAI and it shows. Maybe in the future they will focus on it

Yes, when new technology comes you may need to upgrade. OpenAI aren't hero's but they are covering the cost to move people from old to new embedding models

Setting up an embedding alternative out of huggingface sentence transformers is fairly easy, the magical thing openai does is that they will create embedding of 8192 characters at a time while most other emerging will force you to chunk your documents in 512 characters long sequences, losing lot of context, multiplying your queries result, search times etc