[Fripplebubby]

I think the tricky thing here is that the specific things I referred to (real time writes and pushing SQL predicates into your similarity search) work fine at small scale in such a way that you might not actually notice that they're going to stop working at scale. When you have 100,000 vectors, you can write these SQL predicates (return the 5 top hits where category = x and feature = y) and they'll work fine up until one day it doesn't work fine anymore because the vector space has gotten large. So, I suppose it is fair to say this isn't YAGNI backfiring, this is me not recognizing the shape of the problem to come and not recognizing that I do, in fact, need it (to me that feels a lot like YAGNI backfiring, because I didn't think I needed it, but suddenly I do)

[morshu9001]

If the consequence of being wrong about the scalability is that you just have to migrate later instead of sooner, that's a win for YAGNI. It's only a loss if hitting this limit later causes service disruption or makes the migration way harder than if you'd done it sooner.

[simonw]

And honestly, even then YAGNI might still win.

There's a big opportunity cost involved in optimizing prematurely. 9/10 times you're wasting your time, and you may have found product-market fit faster if you had spent that time trying out other feature ideas instead.

If you hit a point where you have to do a painful migration because your product is succeeding that's a point to be celebrated in my opinion. You might never have got there if you'd spent more time on optimistic scaling work and less time iterating towards the right set of features.

[hobofan]

> When you have 100,000 vectors [...] and they'll work fine

So 95% of use-cases.

[Jnr]

I think Immich (Google photos alternative) uses pgvector. And while you can't really call it a "production" system, because it is self hosted, I have about 100,000 assets there and the vector search works great!

[samus]

In that case you might not even really need optimized vector search though.