Most vector databases treat retrieval as a single operation. That's the wrong abstraction. Storing embeddings and returning nearest neighbors is a solved problem. The hard problem is what happens next. We solve it through composable vector search, built in Rust. Today, led by

Qdrant + Minima: agentic RAG on one RTX PRO 6000 Blackwell GPU. Hybrid search and late-interaction reranking made the first retrieval enough 87% of the time, up from 72%. Median latency 21.3s to 7.7s. 2.92x more successful tasks per GPU-hour. qdrant.tech/blog/case-stud…

We’re coming to New York for an AI debate night where you’ll defend the side you didn’t choose. Join us for Hard Negatives: AI Engineers Debate & Game Night. You’ll draw a controversial AI take at random, get 3 minutes to prepare, and 10 minutes to convince the room. The

How

built an enterprise search engine for 116,000 employees on Qdrant Hybrid Cloud:1 35M points on 4 nodes. Hybrid search for deep agents. Semantic caching to control agent cost. A measured 20% efficiency gain. Three years in production, from prototype to platform:

We’re coming to San Francisco for a night of AI debates, games, and prizes. Join Qdrant and

for Hard Negatives Meetup: Engineers Debate Night. You’ll be paired into teams and randomly assigned a controversial AI topic to defend. You’ll get a few minutes to prepare, then