Case Study: eBay achieves on-demand, privacy-preserving 1GB developer datasets with Tonic.ai

Getting eBay Developers the Data They’re Looking For, with Tonic

eBay faced unreliable staging and poor-quality test data that slowed developer productivity, caused frequent automated-test failures, and made regression testing inconsistent. To solve this, eBay engaged Tonic.ai to deliver advanced subsetting and de-identification — creating referentially-intact, privacy-preserving subsets so developers could access realistic data without risking production privacy.

Tonic.ai implemented a phased rollout, building new database integrations in about one week and producing targeted, referentially-intact subsets that scale an 8‑PB ecosystem down to ~1‑GB clusters (average subset size 1 GB) for critical domains. The result: significant time savings, higher pass rates for automation scripts in staging, faster release velocity, and on-demand access to quality, de-identified data while maintaining privacy.

eBay

Srikanth Rentachintala

Director of Buyer Experience Engineering