ShareChat's engineering team achieved 1000x feature store throughput without expanding their ScyllaDB cluster, a case study in optimization over scaling.
The Indian social platform, serving 400M+ monthly users across ShareChat, Moj, and MX TakaTak, powers its recommendation engine with over 200 production ML models. Initial implementation hit 2 billion rows/sec due to poor data tiling (70 tiles per feature), grinding the system.
Three architectural shifts fixed it. First, data modeling: switching to protocol buffers and Flink processing reduced rows 100x. Second, sharding into 27 services with consistent hashing achieved 95% cache hit rates, later 99%. Third, infrastructure optimization - a forked FastCache implementation (100x less contention), gRPC buffer pooling, and garbage collection tuning.
Final load: 18.4 million rows/sec to serve 1 billion features/sec. The original ScyllaDB cluster was over-provisioned and later downsized.
This matters because feature stores are infrastructure bottlenecks as ML deployments scale. They handle real-time aggregations (1-hour to 30-day windows) for recommendation systems, requiring single-digit millisecond latency. The pattern - optimize aggressively before scaling - contradicts vendor advice but matches what works in production.
ShareChat runs a 100+ person ML team. The company hasn't disclosed when these optimizations occurred, though ScyllaDB published the case study this week.
Broader context: 32% of ML/AI use cases are production-ready according to recent surveys, up from 15% year-over-year. Feature stores are becoming critical infrastructure as agentic AI systems require persistent state management. Alternative approaches include Feast with Redis caching, Databricks Feature Store materialization strategies, and SageMaker's offline/online serving split.
The engineering details suggest most organizations could halve their feature store costs by auditing data models and cache strategies before adding capacity. History suggests they won't.