Moved from Elastic · Vendor case study · Undated · MongoDB Atlas, Atlas Vector Search, Voyage AI
Moved content recommendations from Elasticsearch pipelines to vector search on MongoDB Atlas
Stated outcome: 90% lower latency; 65% lower costs
To manage this complexity, Condé Nast’s engineering team built three separate recommendation pipelines on Elasticsearch.
From Condé Nast Improves Content Recommendation: +35% CTR | MongoDB, mongodb.com.Checked Sep 23, 2026 Condé Nast stack · MongoDB customers
| What |
|---|
| Jul 2019Unified consumer view and global reporting across 37 brands such as Vogue and GQ |
| UndatedAutomated ingestion from 20+ sources into Databricks Delta Lake for audience segments |
| What |
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| Sep 2026Call order for MongoDB - blanket purchase agreement 28321321a00040003 |
| Aug 2026Scaled high-throughput payment aggregation on Atlas for Big Billion Days sales |
| Aug 2026Moved its Prisma SASE health portal to a three-region Atlas cluster for zero-downtime failover |
| Jul 2026Built an AI incident investigation tool that matches issues to historical context |
| Jul 2026Powered RAG for its Webex virtual assistant over 3,600 help articles |
| Jul 2026Built a persistent memory layer for its Lenai AI platform used across 90,000 employees |
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