8M+ TPS
Sony powers PlayStation personalization for 100M+ active users at under 10ms latency
White paper
Real-time recommendation systems built for the scale you didn't plan for
Get the blueprint for combining offline ML models with an online feature store database to deliver personalized recommendations in milliseconds. See how leading platforms build real-time recommendation engines with low-latency feature serving that stay fast, accurate, and available at global scale.
Serve personalized recommendations with sub-millisecond feature lookups
Connect offline model training with real-time inference at scale
See how Sony, Myntra, Wayfair, and Rakuten built real-time systems
A blueprint for real-time recommendation systems
Modern recommendation engines must turn live signals into personalized results in milliseconds. That requires a database for real-time inference that can retrieve relevant features, rank results in milliseconds, and maintain predictable low latency as data volume and traffic grow globally.
This white paper is a practical guide to building or modernizing a real-time recommendation systems. Learn how high-performing recommendation engines connect offline machine learning to an online real-time feature store database, support multi-stage retrieval and re-ranking, and sustain low-latency performance as traffic grows. Whether you personalize a homepage, product feed, content queue, or checkout flow, this guide shows how the architecture helps applications act on current behavior. Not stale model inputs.
Real-time feature access at sub-millisecond speed
Recommendation quality depends on how fresh and fast your features are. Aerospike's real-time feature store delivers the low-latency reads modern inference pipelines need, so recommendations reflect what a customer is doing right now — not what they did yesterday.
Personalization built for retail, streaming, and adtech at scale
From e-commerce product feeds to streaming content queues to real-time ad targeting, every industry now competes on relevance. See how real-time recommendation infrastructure supports multi-stage ranking, edge and core deployment, and messy, incomplete, or conflicting data — without slowing down the customer experience.
30TB+ of data, sub-10ms lookups
Wayfair powers real-time product recommendations across the full shopping journey
One database for offline training and real-time inference
Model training and live serving don't have to run on separate systems. Aerospike's Hybrid Memory Architecture lets teams train on historical data and serve real-time predictions from the same high-performance foundation — reducing complexity and infrastructure cost.
Aerospike's flash-optimized, in-memory design was well suited to the personalization scale and latency requirements Sony needed for PlayStation.
"We don't get the luxury of shallow promises." Wayfair's supply chain team relies on Aerospike to precompute accurate fulfillment paths in real time, even as trucks close or capacity shifts.
Since standardizing on Aerospike's Kubernetes Operator, Flipkart has seen "100% availability with the operator," with zero production incidents traced back to it.