Case Study: Dust achieves sub-second vector search and 5,000+ data source scale with Qdrant

Dust slashes search latency from 5–10 seconds to under 1 second with Qdrant

Dust, an OS for AI-native companies, faced significant scaling challenges as their platform grew to manage over 5,000 data sources. Their initial infrastructure, which used a separate vector collection for each source, led to severe performance degradation, high RAM consumption, and slow search times. To address this, they sought a new vector database solution and chose Qdrant.

By implementing Qdrant, Dust consolidated thousands of collections into a few multi-tenant ones and utilized features like scalar quantization. This reduced their vector storage size by four times and slashed search latency from 5-10 seconds to under a second. The results included a dramatic drop in RAM usage and a more responsive user experience, allowing Dust to scale its operations efficiently and with greater flexibility.

Dust