Optimizing a Fast Feature Store for Costs: ShareChat's Lessons Learned (opens in new tab)

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.