- CAST AI introduced two new features, Workload Rightsizing, and PrecisionPack, to further enhance its Kubernetes cost optimization platform and advance automation within Kubernetes environments.
- Workload Rightsizing, one of the newly introduced features, automates resource scaling to alleviate the challenge of accurately predicting and adapting resource needs for Kubernetes workloads.
CAST AI Group Inc., a Kubernetes operations and cost management startup, revealed its successful USD 35 million funding round and introduced two innovative features during KubeCon CloudNativeCon in Chicago.
This fresh funding injection will further empower CAST AI to advance its Kubernetes cost optimization platform, enabling expanded functionalities, faster innovation, and delivering increased value in terms of customer savings and productivity. Kubernetes manages container-based apps that can be built once and run on any platform.
CAST AI, established in 2019, provides a cloud optimization platform that slashes cloud costs by 50% for Amazon Web Services Inc., Google Cloud, and Microsoft Azure customers. Leveraging artificial intelligence, the platform swiftly assesses various data points to determine an optimal cost-performance ratio, completing the optimization process within minutes.
By integrating their Kubernetes clusters with the CAST AI platform, organizations gain access to recommended cost-saving strategies and immediate deployment of cloud-native automation techniques. Prominent CAST AI clients encompass Hitachi Ltd., Samsung Next LLC, Forbes Media LLC, Snow Commerce LLC, Akamai Technologies Inc., Surfshare Inc., Yotpo Ltd., and Delio Ltd.
Vintage Investment Partners LLC spearheaded the Series B funding round, with participation from existing investors Creandum AB and Uncorrelated Ventures LLC.
“Every single person at CAST AI is relentlessly focused on helping customers slash their cloud spend by automating tasks that are best performed by machine learning systems,” Yuri Frayman, Co-founder and CEO, stated before the news announcement. “That’s why customer growth continues to accelerate and we’ve recently welcomed marquee customers like Akamai and Yotpo. The new funding will further bolster customer savings and productivity as we expand our platform’s capabilities and automate even more aspects of Kubernetes.”
In addition to securing fresh funding, CAST AI introduced two new features – Workload Rightsizing and PrecisionPack- to further enhance its Kubernetes cost optimization platform and advance automation within Kubernetes environments.
Workload Rightsizing, one of the newly introduced features, automates resource scaling to alleviate the challenge of accurately predicting and adapting resource needs for Kubernetes workloads. This feature addresses a common issue in cloud management. It dynamically adjusts workload requests in near real-time, optimizing application performance while avoiding unnecessary expenses caused by over-provisioning.
PrecisionPack specifically addresses the placement of pods within Kubernetes clusters. PrecisionPack focuses on strategically allocating and scheduling pods, the smallest computing units in Kubernetes, across different nodes within the cluster. This ensures optimal resource utilization and efficient workload distribution.
PrecisionPack uses an advanced bin-packing algorithm to allocate pods to nodes intelligently, optimizing resource utilization and minimizing inefficiencies. CAST AI claims that PrecisionPack improves cluster efficiency and enhances workload predictability and stability.