Kubernetes operations and cost management startup CAST AI Group Inc. today announced that it has raised $35 million in new funding and also debuted two new features at KubeCon + CloudNativeCon in Chicago.
The new funding will be used to enhance CAST AI’s Kubernetes cost optimization platform by expanding capabilities, accelerating innovation and improving customer savings and productivity. Kubernetes is software that manages container-based apps, which can be built just once and run on any computing platform.
Founded in 2019, CAST AI offers a cloud optimization platform that is claimed to cut cloud bills in half for Amazon Web Services Inc., Google Cloud and Microsoft Azure customers. The platform uses artificial intelligence to analyze multiple data points to find an optimal cost-performance ratio, optimizing them in minutes.
Organizations that connect their Kubernetes clusters to the CAST AI platform are able to see suggested recommendations and access cloud-native automation techniques for immediate cost reduction. Notable Cast AI customers include Hitachi Ltd., Forbes Media LLC, Samsung Next LLC, Snow Commerce LLC, Surfshare Inc., Akamai Technologies Inc., Yotpo Ltd. and Delio Ltd.
The Series B round was led by Vintage Investment Partners LLC, with existing investors Creandum AB and Uncorrelated Ventures LLC also participating.
“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,” co-founder and chief executive officer Yuri Frayman said ahead of the release of the news. “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.”
Along with its new funding, CAST AI today announced two new features, Workload Rightsizing and PrecisionPack, that it says advance its Kubernetes cost optimization platform and signal a stride toward a fully automated Kubernetes environment.
Workload Rightsizing is a feature that automates the scaling of resources and addresses a common pain point in cloud management: the difficulty of accurately predicting and adjusting the resources required for Kubernetes workloads. The feature dynamically resizes workload requests in near real-time, ensuring that applications perform optimally without incurring unnecessary costs due to over-provisioning.
PrecisionPack is focused on pod placement within Kubernetes clusters. That refers to the strategic allocation and scheduling of pods — the smallest deployable units of computing that can be created and managed in Kubernetes — across various nodes within the cluster for optimal resource utilization and workload distribution.
By employing a sophisticated bin-packing algorithm, PrecisionPack strategically allocates pods to nodes, optimizing resource utilization and minimizing waste. According to CAST AI, this not only boosts the efficiency of the cluster but also enhances the predictability and stability of workloads.
Image: CAST AI
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