I was getting 200ms latency on vector search with only 50,000 embeddings. For a drone that needs to recognize objects in <50ms, that's not a database — that's a liability.

So I did what any reasonable developer would do. I spent 3 weeks tuning HNSW parameters. ef_search , M , ef_construction — I tried every combination. I switched to IVF. I tried PQ (product quantization). I even implemented a custom filtering layer to skip low-score candidates early.

Nothing moved the needle. 180ms. 190ms. 210ms if the CPU was busy with sensor fusion.

Then I realized the problem wasn't the search algorithm. It was the index structure itself — and the fact that I was treating an embedded database like a server database.

The Setup: Vector Search on a Drone

I'm building moteDB, an embedded multi-modal database for edge AI.…