Seamless integration with cost reporting suite, instance and lifecycle management and
innovative scheduling algorithms propels CAST AI’s cost-aware autoscaling to new heights

A black screen with a text that reads,'cst ai accelerates fully with the launch of kobe, leveraging Kubernetes for workload rightsizing.

Today at KubeCon Chicago 2023, CAST AI, the leading Kubernetes cost optimization platform, unveiled two pivotal new platform capabilities – Automated Workload Rightsizing and PrecisionPack. This announcement was made in tandem with a successful $35 million Series B round from Vintage Investment Partners and existing investors Creandum and Uncorrelated Ventures, which will further accelerate CAST AI’s pace of innovation. 

“Our vision transcends individual features. We are on a mission to deliver a fully automated Kubernetes experience. The introductions of Workload Rightsizing and PrecisionPack is a significant stride towards that horizon,” said Laurent Gil, co-founder and chief product officer of CAST AI. “It’s not just about rightsizing; it’s about creating a synergistic ecosystem where every component – from cost reporting to instance management – works in unison to deliver unprecedented value.”

These new features are not just standalone capabilities; they are significant drivers in a robust platform. When integrated with CAST AI’s Cost-Aware Autoscaler, Spot Instance Management and the complete Cost Reporting Suite, it forms a powerful combination that drives cost-efficiency and performance optimization to the next level. Automated Workload Rightsizing ensures that Kubernetes workloads are not just efficiently sized but are managed in a cost-aware manner, embodying a holistic approach to cost optimization and resource utilization.

Accurately forecasting the resources required for Kubernetes workloads is complex, and has traditionally been a stumbling block for organizations that required a lot of human intervention. The propensity to under or over-provision resources leads to operational risks and resource wastage. Workload Rightsizing overcomes this hurdle by automating the scaling of workload requests in near real-time, ensuring optimal performance while being cost-effective.

Resource allocations are regenerated in near real-time, offering granular control and configurability per workload. The flexibility extends to specifying additional overhead for CPU and RAM, adjusting percentile values and setting thresholds for applying these automatically. CAST AI’s product roadmap includes the introduction of seasonality models to better predict resource needs across varying time cycles, further bolstering response time and availability.

Alongside Workload Rightsizing, CAST AI also introduced PrecisionPack – a next-generation Kubernetes scheduling approach that eradicates randomness in pod placement. It employs a sophisticated bin-packing algorithm to ensure strategic pod positioning onto the designated set of nodes, maximizing resource utilization, while bolstering efficiency and predictability across Kubernetes clusters. Workload movement is reduced, which improves both uptime and reliability of workloads, while maintaining a perfect blueprint for cluster cost optimization.

For a deeper dive into CAST AI’s enhanced offerings and to understand how Workload Rightsizing and PrecisionPack dovetail into the company’s broader vision of Kubernetes automation, click here.

The transformative value CAST AI brings to the cloud ecosystem hasn’t gone unnoticed. Gartner recently recognized CAST AI as a Cool Vendor in the 2023 Cool Vendors™ in Cloud That Drive Business Disruption report.

Gil added, “The journey has just begun. The recent capital raise has supercharged our pace of innovation. As we continue to unfold platform capabilities that encapsulate our vision of a fully automated Kubernetes reality, the value proposition for our customers becomes significantly amplified.”

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About CAST AI:

CAST AI is the leading Kubernetes cost optimization platform for AWS, GCP and Azure customers. The company’s platform goes beyond monitoring clusters and making recommendations; it utilizes advanced machine learning algorithms to analyze and automatically optimize clusters, saving customers 50% or more on their cloud spend, improving performance and reliability and boosting DevOps and engineering productivity.

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