Case Study: iHerb reduces infrastructure and manpower costs with Cribl Stream

iHerb manages 5TB/day and cuts observability costs with Cribl

iHerb, a major online retailer of health and wellness products, faced significant challenges managing its growing volume of observability data. Its in-house, open-source data platform consumed excessive engineering resources, creating technical debt and high costs associated with infrastructure, latency, and downtime. To solve this, iHerb turned to the vendor Cribl and implemented Cribl Stream to build a more optimized observability pipeline.

By implementing Cribl Stream, iHerb gained the ability to selectively route, reduce, and transform 5TB of daily data. The solution intelligently filtered data to destinations like Splunk, Elastic, and Loki, reducing the load on these tools and cutting infrastructure costs. Cribl also provided critical security features like data masking and a replay function. The results included increased uptime, faster search returns, and major savings on engineering time and resources, allowing iHerb's team to focus on more critical tasks.

iHerb