How Musinsa scaled its audience engine with ClickHouse Cloud and reduced TCO by 71.4%

Summary

Musinsa’s Audience Engine powers a customer data platform that targets audiences across 16.4 million members. It stores each user-to-audience relationship as a separate row, producing roughly 1.1 billion cohort-to-user mappings across 2,500 audiences and potentially 16 million new rows when one audience is added. Its self-hosted ClickHouse deployment coupled storage and compute, forced workloads to share a cluster, and created operational burdens as data and query demands grew. Musinsa migrated to ClickHouse Cloud on AWS, using separated S3-backed storage, workload-specific compute, automatic idling, vertical autoscaling, and ClickPipes for direct real-time ingestion. The company reports an 86.5% reduction in storage costs and a total cost of ownership reduction of up to 71.4%, while shifting engineering effort toward CDP business logic.

Takeaways

1. 1

   Musinsa’s exploded audience representation contains roughly 1.1 billion cohort-to-user mappings across more than 16 million users and 2,500 audiences.

2. 2

   Separating compute from shared S3 storage let Musinsa scale storage independently, avoid adding unnecessary storage when adding compute, and reduce existing storage costs by 86.5%.

3. 3

   ClickPipes replaced Databricks Auto Loader on an external Spark cluster, simplifying direct real-time ingestion and reducing operational overhead and Databricks costs.