Azul & Cast AI link up to cut Java cloud costs by 80%
Azul and Cast AI have entered a strategic partnership to enhance Java application performance and reduce cloud infrastructure costs for enterprises running Java workloads in Kubernetes environments.
The collaboration brings together Azul's Platform Prime and Cast AI's Application Performance Automation platform, offering a combined solution aimed at improving Java runtime efficiency, trimming compute and memory usage, and cutting costs by as much as 80%.
Performance and cost challenges
Many organisations deploying Java-based applications at scale face two persistent issues: sustaining application performance across varied workloads and maintaining control over escalating cloud infrastructure expenses. The partnership between Azul and Cast AI intends to address both these concerns with a unified approach tailored for Kubernetes-based public cloud deployments.
Azul's Platform Prime delivers a high-performance Java runtime that supports faster application startup times and ensures consistent runtime behaviour. This is integrated with Cast AI's platform, which employs real-time automation to analyse and automatically adjust Kubernetes cluster resources based on actual demand. Cast AI's system aims to eliminate both overprovisioning and underutilisation, thereby promoting more efficient use of allocated compute resources.
Automation and efficiency
The companies assert that DevOps and platform engineering teams will be able to use the combined suite without needing to rewrite code, rearchitect applications, or perform manual tuning. Automated right-sizing of clusters, carried out by Cast AI's agents, matches capacity to intended workload in real time and seeks to control both cost and operational risk.
On the performance side, the collaboration provides enterprises with capabilities to enhance code execution and maintain consistent speed. Azul's Java platform, when paired with Cast AI, is designed to guarantee rapid workload deployment and steady, peak runtime performance in unpredictable cloud environments.
Cloud savings
The companies report that, by leveraging the infrastructure scaling of Cast AI and the runtime efficiencies of Platform Prime, infrastructure costs can be brought down by up to 80% without jeopardising the speed or reliability of applications. Cost savings are intended to be achieved without compromising on operational stability.
In addition to performance and cost optimisation, the joint solution offers security and resilience features. The partnership aims to provide built-in automation and performance-optimised configurations to help enterprises manage their workloads securely, reliably and cost-effectively, all while minimising the operational overhead for developers and DevOps professionals.
Statements from company leaders
"For many organizations, cloud costs now rank among the largest infrastructure expenses," said Laurent Gil, co-founder and president of Cast AI. "This partnership combines Cast AI's autonomous agents with Azul's high-performance Java platform to automatically eliminate cloud waste and boost application performance. Together, we're helping customers dramatically reduce spend while improving reliability and operational efficiency - all with the help of AI agents and without changing a single line of code."
Scott Sellers, co-founder and Chief Executive Officer at Azul, said, "Java is at the heart of enterprise applications, and Kubernetes is the de facto platform for deploying them. By combining Azul's expertise in Java performance and efficiency with Cast AI's Application Performance Automation platform, customers can dramatically reduce cloud costs while maintaining the speed and reliability that modern businesses demand."
The partnership focuses on providing a holistic solution to Java workload optimisation without increasing development effort or the need for manual intervention, aiming to support enterprises in managing performance, security and costs within their cloud environments.
