A blueprint for real-time recommendation systems
Deliver personalized experiences in the moment
Today’s recommendation engines must operate at scale under increasing complexity: more data, noisier signals, and customer expectations for instant, relevant engagement. Whether it’s personalizing a homepage, finding the right product at checkout, or tailoring a content feed, businesses must act on limited signals in milliseconds, or risk losing the moment.
This white paper outlines a practical blueprint for building and updating real-time recommendation systems that scale with today’s demands.
What you'll learn inside
Architectural foundations
How today’s recommendation systems combine offline and real-time components to support fast, relevant decisions
The critical role of the feature store
Why low-latency feature access is essential for scalable inference pipelines
Design insights under real-time pressure
How modern systems handle multi-stage retrieval and re-ranking, edge and core deployment strategies, and incomplete or conflicting data
Infrastructure savings
How Aerospike’s patented Hybrid Memory Architecture delivers sub-millisecond reads at scale, high throughput, and global availability, at a fraction of the cost of memory-only systems
Real-world examples
Learn how companies such as Sony Interactive Entertainment, Myntra, Quantcast, Wayfair, Rakuten, and Flipkart power global-scale personalization with Aerospike
From signals to decisions in milliseconds
Companies that fail to deliver timely, relevant experiences lose customers to competitors that do. This blueprint shows how to:
Respond quickly to session-level signals, even when data is incomplete
Scale recommendation pipelines without exploding costs
Move beyond experimental architectures to systems proven in production
Equip your team with a practical framework to build recommendation systems that meet today’s real-time demands and tomorrow’s scale.
A blueprint for real-time recommendation systems
Today’s recommendation engines face a tough challenge: more data, noisier signals, and customers who expect instant relevance. Many systems struggle with latency, cost, or scale when the pressure is on. This blueprint walks through the architectural foundations of real-time recommendations, the role of the feature store, and how Aerospike delivers sub-millisecond performance for personalization at global scale.
