Designing an Efficient Real-Time Feature Store at Zomato

Zomato is one of India's leading food delivery and restaurant discovery platforms, serving millions of customers across hundreds of cities. Its real-time feature store powers recommendation and personalization ML models by storing and serving behavioral, historical, and contextual features for customers, restaurants, dishes, cuisines, and delivery partners at massive scale.

To support growing traffic and increasingly demanding ML feature-data workloads, Zomato redesigned its real-time feature store to handle approximately 40M RPM while significantly reducing infrastructure costs, improving latency, and scaling efficiently through optimized serialization, intelligent caching, and concurrency-aware request handling.

In this session, we will share the architectural decisions and engineering techniques behind the redesign, including Protocol Buffer optimization, DynamoDB storage and network efficiency improvements, caching-layer optimizations, and request coalescing to eliminate duplicate concurrent requests. We will also discuss the key architectural trade-offs, operational challenges, and lessons learned from running a production-grade feature store at scale.

Attendees will gain practical insights into building large-scale, low-latency ML feature stores, with a focus on infrastructure efficiency and designing systems that scale reliably with evolving workloads.