Case Study: Tinder achieves 4x lower CPU usage and faster real-time recommendations with Elastic

Tinder cuts CPU usage 4x with Elastic

Tinder, one of the world's largest online dating platforms, faced the challenge of scaling its real-time recommendation engine to serve millions of users across 190 countries. The company needed to quickly retrieve and rank tens of millions of profiles based on location, preferences, and user history while managing infrastructure costs and maintaining extremely low latency for its users. To address this, Tinder built its large-scale recommendation architecture using the vendor Elastic's open source search engine, Elasticsearch.

By leveraging Elasticsearch on Elastic Cloud on Kubernetes, Tinder implemented a multi-stage pipeline with several custom optimizations. These included geo-indexing to reduce the search scope and a Roaring bitmap filter to efficiently exclude profiles users had already seen. These solutions from Elastic, combined with custom machine learning integrations, resulted in a 60% improvement in filtering performance, a 4x reduction in CPU usage, and a 47% decrease in scoring latency, enabling Tinder to deliver personalized matches globally in milliseconds.

Tinder