{"id":52615,"date":"2026-03-27T18:00:00","date_gmt":"2026-03-27T11:00:00","guid":{"rendered":"https:\/\/thaipropertynews.com\/feeds\/?p=52615"},"modified":"2026-03-27T18:00:00","modified_gmt":"2026-03-27T11:00:00","slug":"zilliz-cloud-launches-native-cross-region-disaster-recovery-for-vector-databases","status":"publish","type":"post","link":"https:\/\/thaipropertynews.com\/feeds\/?p=52615","title":{"rendered":"Zilliz Cloud Launches Native Cross-Region Disaster Recovery for Vector Databases"},"content":{"rendered":"\n\n\n
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New Global Cluster and Global Endpoint capabilities deliver automated failover with near-zero downtime for enterprise AI applications<\/i><\/p>\n

REDWOOD CITY, Calif.<\/span>, March 27, 2026<\/span> \/PRNewswire\/ —\u00a0Zilliz<\/a>, the company behind Milvus<\/a>, the world’s most widely adopted open-source vector database<\/a>, today announced native cross-region disaster recovery for Zilliz Cloud<\/a>, making it the only vector database<\/a> to offer automated cross-region failover with near-zero data loss and sub-60-second recovery times.<\/p>\n

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Recent high-profile cloud outages \u2014 including simultaneous AWS region failures in the Middle East, a 14.5-hour Azure outage, and cascading infrastructure incidents costing Fortune 500 companies billions \u2014 have exposed a critical gap in AI infrastructure resilience. With nearly 60% of employees now relying on AI-powered workflows daily, region-level failures no longer cause gradual slowdowns; they trigger immediate productivity cliffs across the enterprise.<\/p>\n

“Cloud regions will fail \u2014 that’s not a prediction, it’s an operational certainty. What matters is whether your AI infrastructure can recover in seconds rather than hours. We built cross-region disaster recovery natively into Zilliz Cloud so that enterprises never have to choose between the performance of vector search and the resilience their applications demand.” said Charles Xie, Founder and CEO at Zilliz.<\/b><\/p>\n

Why Cross-Region Disaster Recovery Matters for AI<\/b><\/p>\n

Vector databases power critical enterprise AI workloads \u2014 from RAG<\/a> pipelines and semantic search<\/a> to recommendation engines and AI agents. Unlike stateless model inference, vector indexes containing hundreds of millions of vectors can take 18+ hours to rebuild, making traditional disaster recovery approaches impractical. Zilliz Cloud’s new capabilities address this with three complementary solutions:<\/p>\n