HubSpot分享了如何用Qdrant处理20B+向量搜索，自己写K8s operator搞定Helm搞不定的集群管理，集群创建从小时变分钟，资源偏差降65%。硬核实战经验。
HubSpot基于Qdrant构建了VAST向量搜索服务，管理超过20B向量，覆盖5个区域，服务38个团队。架构包含150个集群、2000多个pod，单个集合达9.5B向量，写入速度5000/s峰值100K/s。为解决Helm的局限性，他们开发了针对Qdrant的Kubernetes operator，实现了分片管理、复制和生命周期自动化，集群创建从小时级缩短至分钟级。在一项3B+点的BM42稀疏向量集合上，资源偏差降低了65%。
How do you run vector search at 20B+ vectors across 5 regions for 38 teams? @HubSpot built VAST - V...
How do you run vector search at 20B+ vectors across 5 regions for 38 teams? @HubSpot built VAST - Vector as a Service, entirely on Qdrant. 150 clusters, 2K+ pods, 9.5B vectors in a single collection, 5K writes/sec with spikes to 100K. the real story: they outgrew Helm fast. Helm can't call the Qdrant API to transfer shards, maintain replication factor, or handle state-aware scaling. cluster creation took hours. so they built a Kubernetes operator specifically for Qdrant. shard management, replication, lifecycle automation, all handled automatically. cluster spin-up: hours → minutes. result: 65% reduction in resource skew on a 3B+ point BM42 sparse vector collection. full talk here: youtube.com/watch?v=46aQff… thanks Oleg Tereshin and Xin Liu from @HubSpot team, for sharing at Vector Space Day SF 🙌 💬 1 🔄 0 ❤️ 3 👀 117 📊 2 ⚡