Summary
Standard Java runtimes place performance and scalability constraints on retailers. These constraints can increase retail platforms’ page load times and cart abandonment rates, while limiting retailers’ ability to fraud prevention, AI tools, and other features that can optimize retail performance but increase cloud costs. In this post you will learn:
- Features that require heavy use of compute resources can increase cloud costs
- Azul customers routinely save 20% or more on cloud spend
- Azul Platform Prime accelerates advanced fraud detection, enabling analysis in microseconds
- Platform Prime customers can build AI-driven systems without high cloud costs
Retail and e-commerce leaders use Azul Platform Prime, Azul’s high-performance Java platform, to overcome performance and scalability constraints of standard Java runtimes. Azul customers routinely save 20% or more on cloud spend by reducing the number of JVMs required to deliver consistent peak performance. This leads to faster page loads, real-time personalization, and lower cart abandonment rates, especially during high-traffic periods like holiday sales or major product drops.
How you can derive value from Azul Platform Prime:
- Stability at Scale: Maintain performance during flash sales, seasonal spikes, or social traffic surges without overprovisioning infrastructure.
- Lower Cloud Costs: Reduce the need to overprovision infrastructure, often cutting compute and memory expenses by 20% or more.
- Improve Throughput: Increase transaction volumes per JVM instance, enabling retailers to handle more users with fewer servers.
- No Code Changes Required: Improve app performance simply by replacing the runtime — no refactoring, recompilation, or retraining needed.
- Backed by FinOps Principles: Azul is a FinOps Foundation member, helping organizations optimize cloud usage and align spend with business outcomes.
Azul Platform Prime powers advanced fraud detection
In the battle against fraud, speed and precision are critical. Platform Prime significantly accelerates advanced fraud detection processes, enabling analysis in microseconds even under high transaction volumes and demanding compute thresholds.
- Acceleration of Fraud Detection: Accelerate advanced fraud detection algorithms in microseconds, reducing the window for fraudulent transactions to be processed and approved.
- Scalability: Enable fraud detection systems to seamlessly handle the surge in transaction volumes during peak retail seasons, promotions, and flash sales without performance degradation.
- Reduced Latency: Minimize latency within the data processing pipelines that feed fraud detection models. This allows for more up-to-date data to be analyzed, leading to more accurate and effective fraud identification.
- Lower Infrastructure Costs: Azul Platform Prime’s efficient memory management and reduced garbage collection overhead allow retailers to achieve higher fraud detection throughput with fewer compute resources in the cloud or on-premises, leading to significant cost savings.
Accelerate AI-driven retail and social commerce with Azul
Azul Platform Prime optimizes Java performance to enable retailers to build responsive, intelligent, and scalable systems that adapt to shopping behavior in real time. By switching to Azul’s high-performance Java platform, retail and commerce platforms gain powerful advantages across AI, big data, and customer engagement use cases.
- Real-Time Personalization: Ultra-low latency ensures personalization engines respond in real time to changing customer behavior, from product recommendations on social platforms to individualized promotions during live shopping events.
- Optimizes Big Data Pipelines: Reduce resource overhead with advanced JIT compiler and memory management improvements enabling data ingestion, enrichment, and analysis to run faster and more efficiently.
- Consistent Performance: Ensure consistent, pause-free performance under unpredictable, high-volume traffic during flash sales, influencer promotions, or viral moments on social platforms with the C4 garbage collector.
- Intelligent Shopping Experiences: Rapid development and reliable performance of next-generation shopping features—like embedded marketplaces, third-party ad engines, and algorithmic merchandising across social ecosystem
