Optimizing a Fast Feature Store for Costs: ShareChat's Lessons Learned

This story was originally published on HackerNoon at: https://hackernoon.com/optimizing-a-fast-feature-store-for-costs-sharechats-lessons-learned. After scaling its ML feature store to 1B features/sec, ShareChat cut costs 10× using ScyllaDB, cloud tuning, protobuf tricks, and profiling. Check more stories related to undefined at: https://hackernoon.com/c/undefined. You can also check exclusive content about #scylladb-feature-store, #sharechat-cloud-cost-reduction, #ml-feature, #scylladb-workload, #kubernetes-cost-optimization, #network-egress-optimization, #feature-store-architecture, #good-company, and more. This story was written by: @scylladb. Learn more about this writer by checking @scylladb's about page, and for more stories, please visit hackernoon.com. After scaling its real-time ML feature store from 1M to 1B features per second, ShareChat faced a new challenge: make it 10× cheaper. The team attacked costs across every layer—cleaning cloud waste, moving away from expensive managed databases, optimizing Kubernetes utilization, reducing inter-zone network charges, prioritizing ScyllaDB workloads, and redesigning protobuf handling. Continuous profiling and lazy deserialization delivered major compute savings without sacrificing latency or scale.