Komodor extended its reliability-first cost optimization capabilities with AI-based Capacity Intelligence and Predictive Placement to proactively prevent structural inefficiencies and resource waste across cloud infrastructure, allowing SRE teams to unlock up to 80% in cost savings.
Komodor provides a proactive scaling methodology that analyzes workload behavior, scheduler decisions, autoscaler activity, and reliability constraints to improve consolidation, free locked resources, and prevent waste from taking hold. It reclaims stranded capacity caused by Pod Disruption Budgets, anti-affinity rules, unevictable workloads, and non-terminating nodes that prevent consolidation. Komodor also eliminates node bloat from scheduling decisions that place workloads on nodes that should be drained, which anchors capacity and forces clusters to grow larger than necessary.
“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.”
Komodor's two new capabilities, Capacity Intelligence and Predictive Placement, form a continuous loop that detects these inefficiencies, diagnoses their root causes, remediates them, and prevents new waste from taking hold.
Capacity Intelligence continuously scans Kubernetes environments to autonomously identify cluster-level issues that prevent node consolidation by detecting underlying configuration issues, such as disruption-policy conflicts, unevictable workloads, and inefficient anti-affinity rules. Each recommendation delivers clear root cause analysis with a quantified financial impact summary that is easy for non-experts to understand, as well as one-click remediation with built-in reliability validation and safety guardrails to protect production stability.
Predictive Placement 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. It also intelligently places unevictable workloads onto designated nodes to improve autoscaler efficiency and increase node consolidation opportunities.
Because these capabilities are integrated into the Komodor AI SRE platform, every optimization recommendation is evaluated using Klaudia Agentic AI technology, enabling engineering teams to optimize cloud costs without introducing instability, performance degradation, or operational risk.
The new Capacity Intelligence and Predictive Placement cost optimization capabilities are available immediately within the Komodor platform.
The Latest
In 2026, the cost of downtime or an outage is no longer just a technical inconvenience; it's a $600 billion wake up call for global businesses. As our digital ecosystems become more interconnected, each touchpoint introduces new risks and multiplies the consequences when things go wrong. And the data is clear: aggregate downtime costs for Global 2,000 companies have surged 50% since 2024, reaching a staggering $600 billion ...
Deloitte found that 74% of enterprises expect to deploy agentic AI solutions in the next 24 months. However, the rush to deployment is outpacing foundational work, though. Only 21% of enterprises have fully formed agent governance models in place. The result? AI agents deployed without guidance or governance begin to function as fragmented islands of complexity ...
Cloud spending is no longer viewed as a passthrough IT expense, but as a strategic financial lever that directly impacts innovation capacity, profitability and enterprise resilience, according to the CFO Cloud Cost Optimization Report from Azul ...
As AI moves from generating responses to performing actions, the need for trust increases exponentially. And as organizations enlist AI agents for increasingly sophisticated business processes, trust is going to be the single most important theme for spurring adoption. What can organizations do to build trustworthy AI agents? ...
I've spent a lot of time in the channel, and one thing I keep coming back to is this: a partner program is only as good as what it looks like in the field. Many programs look great on paper, but when a partner is in front of a customer navigating a complex hybrid environment or trying to make the case for AI-powered observability, the gap between what a vendor promises and what it actually delivers becomes very clear, very fast ...
Enterprises today operate in a real-time environment where uninterrupted access to trusted data has become a baseline expectation for users, applications and automated systems. Traditional DataOps models, built on manual effort and human triage, cannot keep pace with this always active demand. AI agents are emerging as the operational backbone, ensuring consistent data availability, reinforcing trustworthiness and enabling a level of scale that manual processes cannot achieve ...
For decades, trust in the digital workplace rested on familiar signals. We trusted faces on video calls, voices on the phone, and emails that appeared to come from people we knew. These cues felt human and intuitive. They anchored how decisions were made, approvals were granted, and access was authorized. AI-powered deepfakes have quietly broken that model ...
Cloud migration was supposed to be a one-way door. For most enterprises, it turns out it isn't. Cloud data repatriation is a real and growing trend. A new survey ... finds that 89% of organizations plan to expand their on-premises infrastructure footprint over the next two years — and 75% have already moved at least some workloads back from public cloud in the past 24 months. The findings point to a broad rethinking of where data belongs ...
Over the past few years, large language models (LLMs) have revolutionized the software industry. Given their ability to excel at multi-step reasoning, LLMs have helped enterprises streamline workflows and adapt to the unknown. However, employing such models comes with sky-high costs, latency issues, and limited flexibility. In the realm of IT operations, it is generally wiser to employ smaller, domain-specific models instead ...
For years, DevOps teams operated under a simple assumption: collect enough telemetry, and you can find and fix any problem. That assumption is breaking down. Modern enterprises now operate across microservices, hybrid cloud environments, APIs, Kubernetes, and highly automated delivery pipelines. Releases happen continuously, dependencies shift constantly, and failures spread faster than teams can diagnose them ...