Cloud solutions Interview Latest

Cloud cost optimization doesn’t compromise performance: CAST AI

January 2, 2025
Jagrati Rakheja
Laurent Gil,
Co-founder and CPO
CAST AI

In a rapidly evolving cloud computing landscape, CAST AI has set its sights on India—one of the world’s fastest-growing technology markets. Focusing on Kubernetes automation, cost optimization, and sustainability, the company aims to address pressing challenges enterprises face in managing cloud resources.

In an exclusive interview with CIO&Leader, Mr. Laurent Gil, Co-founder and CPO at CAST AI shares insights into its expansion into India, the strategic advantages of establishing a presence in Bengaluru, and how its solutions align with India’s goals for eco-friendly, energy-efficient technologies.

CIO&Leader: What inspired CAST AI to expand into the Indian cloud and tech market, and what strategic advantages do you see in establishing a presence here?

    Laurent Gil: India is one of the top 3 key markets for CAST AI due to its strong embrace of Kubernetes and its vibrant developer and DevOps ecosystems. The country’s rapid technological growth, alongside a significant uptick in demand from leading Indian enterprises like ShareChat and Yubi, highlights the need for solutions that optimize cloud costs and enhance operational efficiency.

    By establishing a presence in Bengaluru, we’re strategically positioned to deepen our engagement with India’s thriving Kubernetes community, collaborate more closely with regional partners, and provide tailored customer support. India’s rich talent pool allows us to expand our global workforce further and drive innovation.

    CIO&Leader: In what ways does CAST AI’s approach to cloud sustainability align with India’s goals for eco-friendly and energy-efficient technology solutions?

      Laurent Gil: CAST AI addresses one of the most significant inefficiencies in cloud usage: overprovisioning energy-hungry compute resources. In cloud-native enterprises, average CPU utilization is shockingly low – around 13% – indicating that developers often overprovision their virtual machines (VMs) by eight times. This inflates cloud costs and generates unnecessary carbon emissions due to the excessive energy needed to power and cool underutilized infrastructure.

      CAST AI’s platform solves this by leveraging automation to optimize cloud resources in real-time, ensuring that workloads are right-sized and unused VMs are identified and eliminated. By enabling efficient resource utilization, our solution helps businesses reduce their carbon footprint while cutting costs. This aligns perfectly with India’s sustainability goals, empowering enterprises to adopt greener technologies without compromising performance or innovation.

      CIO&Leader: One of the biggest challenges for CIOs in cloud adoption is optimizing costs while maximizing ROI. How does your organization support enterprises in achieving these goals?

        Laurent Gil: There is a common misconception within the cloud industry that cost optimization is done at the expense of performance. It’s never the case; in fact, it’s always the opposite: the AI engine determines with high precision the amount of computing required by an application and then eliminates, in real-time, unused resources and overcapacity through advanced Kubernetes automation and real-time optimization. Conversely, the AI engine guarantees that applications always have the complete computing capacity if needed. Here’s how we help enterprises achieve these goals:

        1. Automated resource optimization: CAST AI’s platform uses automation to analyze workloads and continuously allocate resources. This ensures businesses only pay for what they use and guarantees high performance.
        1. Eliminating overprovisioning: Overprovisioning is a common yet costly issue. CAST AI identifies underutilized resources and scales infrastructure automatically to match workload demands. Last year, we ran an in-depth analysis of 4,000 clusters running on Amazon Web Services (AWS), Google Cloud Platform (GCP), and Microsoft Azure (Azure). We found that for clusters containing 50 or more CPUs, organizations only utilize 13% of provisioned CPUs and 20% of memory, on average. We ensure resources are right-sized for optimal efficiency.
        1. Improved operational efficiency: By automating routine tasks like workload scaling, node management, and bin packing, CAST AI reduces the operational burden on DevOps teams. This allows businesses to redirect resources toward strategic initiatives, increasing ROI from cloud investments.
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