Summary:
1. Enterprises are wasting billions on unnecessary cloud spending due to inefficiency in cloud usage.
2. Akamai Technologies reduced cloud costs by 40-70% using Cast AI’s Kubernetes automation platform.
3. Cast AI’s APA, powered by ML models, helps optimize application performance, security, efficiency, and cost on multiple cloud environments.
Article:
In the current age of generative AI, cloud costs are soaring to unprecedented levels, primarily because enterprises are not utilizing their compute resources efficiently. Recent projections suggest that enterprises are on track to squander a staggering $44.5 billion on needless cloud spending this year alone. For Akamai Technologies, a company with a complex cloud infrastructure and stringent security requirements, this issue poses a significant challenge.
To address this dilemma, Akamai turned to Cast AI, a Kubernetes automation platform, to optimize costs, enhance security, and boost speed across their cloud environments. The results were remarkable, with Akamai managing to slash between 40% to 70% of their cloud expenses, depending on the workload. Dekel Shavit, senior director of cloud engineering at Akamai, emphasized the importance of optimizing infrastructure continuously without compromising performance, especially when dealing with security events that require real-time responses.
Cast AI’s core platform, Application Performance Automation (APA), leverages specialized agents that monitor, analyze, and take action to improve application performance, security, efficiency, and cost. By integrating into the Kubernetes ecosystem, Cast AI assists customers in scaling clusters, selecting optimal infrastructure, and managing compute lifecycles seamlessly. The platform utilizes machine learning models, reinforced by historical data and learned patterns, to automate tasks and optimize cloud resources effectively.
Akamai’s unique challenges, stemming from their large and intricate cloud infrastructure supporting content delivery and cybersecurity services, necessitated a solution that could optimize costs in real-time across multiple clouds without compromising application performance. Cast AI’s APA features, such as autoscaling, bin packing, workload rightsizing, and spot instance automation, proved instrumental in achieving these objectives. Notably, the integration of spot instances on Spark with Cast AI resulted in significant savings and operational efficiencies for Akamai.
The automation capabilities of Cast AI not only led to substantial cost savings for Akamai but also freed up their DevOps team from manual tuning tasks. This newfound efficiency allowed the team to focus on releasing features faster to customers, rather than managing infrastructure intricacies. By seamlessly integrating Cast AI into their existing workflows, Akamai witnessed a transformative shift in optimizing their Kubernetes infrastructure and streamlining cloud operations.
In conclusion, the success story of Akamai Technologies and Cast AI underscores the importance of harnessing automation and AI-driven solutions to enhance operational efficiency, reduce costs, and improve overall performance in cloud environments. As enterprises navigate the complexities of cloud usage, innovative platforms like Cast AI offer a compelling solution to address the growing disconnect between financial operations and engineering teams.
