# What to Expect: Approximate, Eventually Consistent, Not a General Database — Vector Databases

Source: https://www.geekswithgeeks.com/en/vector-databases/b-expect

> Set realistic expectations about accuracy, consistency and queries.

## Know the trade-offs

Vector databases make trade-offs you should know. **Approximate**: ANN search may miss true neighbours, so recall is a setting you tune. **Consistency varies**: some engines make a new write searchable only after a short delay or after an index segment is built (**eventual consistency**), while a transactional database like PostgreSQL gives normal ACID behaviour, including for vectors. **Limited query power**: dedicated engines are excellent at similarity plus filters but are not general-purpose analytics or transaction engines; joins and aggregations may be missing or basic. **Scores are not probabilities**: a similarity of 0.8 means different things for different models, so choose thresholds from your own data. **Cost**: memory dominates, since most fast indexes want vectors in RAM. Plan for these from the start rather than discovering them in production.

## Test read-after-write on your engine

Insert a record, then query for it in a loop and measure how long until it appears. Do not assume zero delay.

**Quiz:** What does "eventual consistency" mean for a new write in some vector engines?

- [ ] It is searchable before it is written
- [x] It may become searchable after a short delay
- [ ] It is never searchable
- [ ] It replaces the index

*Answer:* It may become searchable after a short delay. Do not assume read-your-writes unless the engine documents it.