AI SRE platform provider Komodor has announced new AI-based capabilities — Capacity Intelligence and Predictive Placement — to help organizations optimize their costs and prevent resource waste across their cloud infrastructures.
Komodor described Capacity Intelligence as an always-on scanner in Kubernetes environments that finds cluster-level issues by looking at at underlying configuration issues. The autonomous AI can then recommend and even remediate those issues with root cause analysis tied to their financial impact that even non-technical experts can understand.
Predictive Placement, Komodor wrote in its announcement, “proactively prevents infrastructure waste before it occurs by guiding scheduling decisions using AI-driven cluster simulations. Operating in front of the Kubernetes scheduler, Komodor continuously evaluates cluster drain scenarios, identifies consolidation candidates, and steers workloads away from nodes likely to become drained or terminated.” The company noted that the capability “also intelligently places unevictable workloads onto designated nodes to improve autoscaler efficiency and increase node consolidation opportunities.”
Komodor saw the need for this kind of functionality because traditional workload rightsizing techniques for CPU and memory requests and using node autoscalers on infrastructure cannot proactively address cost optimization. The company has created a proactive scaling methodology that, it explained, “analyzes workload behavior, scheduler decisions, autoscaler activity, and reliability constraints to improve consolidation, free locked resources, and prevent waste from taking hold.”
“Traditional cloud infrastructure cost optimization is reactive, causing it to miss significant savings opportunities,” said Itiel Shwartz, co-founder and CTO of Komodor. “Because Komodor’s AI SRE has complete awareness of both workload behavior and cluster state, it can prevent structural inefficiencies before they occur and continuously optimize pod placement to maximize cluster utilization. This context-aware approach finally allows teams to eliminate structural waste without risking reliability.”
More than 30% of cluster capacity is typically stranded by optimization blockers, misconfigurations, and autoscaler limitations, which is waste that current cost optimization tools cannot reach, Komodor wrote in the announcement. The new capabiltiies, it said, “form a continuous loop that detects these inefficiencies, diagnoses their root causes, remediates them, and prevents new waste from taking hold.”
Because these capabilities are integrated into the Komodor AI SRE platform, the company’s Klaudia Agentic AI technology evaluates every optimization recommendation, enabling teams to monitor and better control costs,
The new cost optimization capabilities are available immediately.
