Case Study: iHerb Achieves Lower Infrastructure and Data Processing Costs with Cribl Stream

From Open Source to Optimized iHerb’s Data Journey with Cribl

iHerb, the online health and wellness retailer serving millions of customers worldwide, needed a better way to manage rapidly growing log data as volumes doubled to several terabytes per day. Its in-house open-source observability pipeline had become costly, difficult to maintain, and time-consuming for engineering teams, while still needing to support analytics, security, and troubleshooting across multiple destinations.

To solve this, iHerb implemented Cribl Stream, along with Cribl Edge, to selectively route, reduce, transform, and mask data before sending it to S3, Splunk, Elastic, Loki, and Grafana. Cribl helped iHerb cut infrastructure, network, engineering, latency, and downtime costs, reduce load on analysis tools, protect sensitive data with masking, and improve visibility into Kubernetes and system metrics—while processing about 5TB of data per day and getting a POC running in about a week.

iHerb