Here's How ShareChat Scaled Their ML Feature Store 1000X Without Scaling the Database

Written by scylladb | Published 2025/09/24
Tech Story Tags: sharechat-ml-feature-store | scylladb-scaling-case-study | ml-feature-store-optimization | sharechat-moj | low-latency-ml-infrastructure | scylladb-database-optimization | p99-conf-sharechat-talk | good-company

TLDRShareChat scaled its ML feature store from failure at 1M features/sec to 1B features/sec using ScyllaDB optimizations, caching hacks, and relentless tuning. By rethinking schemas, tiling, and caching strategies, engineers avoided scaling the database, cut latency, and boosted cache hit rates—proving performance engineering beats brute-force scaling.via the TL;DR App
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Written by scylladb | Monstrously Fast + Scalable NoSQL. Start Fast. Scale Fearlessly
Published by HackerNoon on 2025/09/24
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