How Dust Scaled to 5,000+ Data Sources with Qdrant

Dust, an operating system for AI-native companies, faced significant challenges in scaling its infrastructure to handle over 5,000 data sources. Initially, their strategy of creating separate vector collections for each data source led to unsustainable RAM consumption and degraded performance. After evaluating several vector databases, Dust selected Qdrant for its open-source Rust foundation, multi-tenancy support, and efficient memory usage. By adopting Qdrant, Dust consolidated its architecture, drastically reducing query latency and RAM usage through features like scalar quantization. This transition enabled Dust to improve the responsiveness and reliability of its AI agents, enhancing user experience and allowing for smoother migrations and model experimentation. The integration of Qdrant also facilitated Dust's ability to scale without compromising user experience, making it a cornerstone of their product roadmap as they continue to evolve their architecture to support new embedding models and expand their customer base.