Main difference is that laya/jev/et-al give you a zero-shot classifier that requires no training. You can prompt engineer your way to a quick fairly reliable cheap enough decision engine that you can use to iterate quickly (by prompt engineering).
Right now a lot of people are doing this with LLMs and it's too slow and expensive.
Imo the right iterative approach to productionizing these systems is something like:
1. Build it with an LLM. Iterate on the prompt
2. Start building a real-world dataset
3. When the prompt works, turn it into a clear rubric for Jev or similar
4. Keep iterating until desired accuracy achieved
5. Use the real-world evals you've built to train a custom classifier fine-tuned to your needs
OpenAI has a section on their embeddings model api page for zero shot classification. Of course you can choose an open weights embedding too if you’d like.
https://developers.openai.com/cookbook/examples/zero-shot_cl...
I think Jev wins on marketing and convenience. Most SWEs don’t want to talk about embeddings, cosine similarity, or precision/recall tradeoffs. They want something which plausibly works and is easy to use.