> Does it also mean that in real world usage, one slight misinterpretation or misevaluation of your metrics and you're liable to 1000x more than you planned to?
Unlikely. You can see why in these two examples that really happened:
One user I spoke with said they assumed "queries per second" is calculated by (number of searches) x (top-k for each search), where "top-k" is the number of results they want back. I don't remember their top-k but let's say it's 10 -- so they were entering a value for "queries per second" that was 10x higher than it should be and they'd see an estimate around 10x higher than they'd really be charged.
Another user thought you get "number of vectors" by multiplying the number of embeddings by the embedding dimensionality (1,536 is a common one). So they were entering a value literally 1,536x higher than they should've. Their actual usage would be calculated (by Pinecone) correctly and not be that high.
Vector dimensionality is a basic concept for AI engineers and QPS is a basic metric for DB admins, but Pinecone sees lots of users who are either new to AI or new to managing DBs or both.