I’m also curious to hear what challenges you encountered. I’ve used LanceDB for a few projects in production now and it’s worked out reasonably well.
The docs quality is spotty, and the lack of parity between the async and sync python API is frustrating, but otherwise it’s been great.
The only performance issues I’ve had have been A) not rebuilding indexes on an appropriate cadence, B) not filtering the search space enough for queries which bypass the index, or C) running search against millions of vectors on an object storage and expecting millisecond latency.
I love lancedb. It’s the only way I’ve found to performantly and cheaply serve 50m+ records of 768 dimensions. Runs on s3 a bit too slow, but on EFS can still be a few hundred millis.
The docs quality is spotty, and the lack of parity between the async and sync python API is frustrating, but otherwise it’s been great.
The only performance issues I’ve had have been A) not rebuilding indexes on an appropriate cadence, B) not filtering the search space enough for queries which bypass the index, or C) running search against millions of vectors on an object storage and expecting millisecond latency.