ScaleOps launches Smart Pod Placement, the first to tackle cost-draining 'unevictable' workloads

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Feb 25, 2025, 09:00 ET

Breakthrough scheduling technology solves Kubernetes' hidden resource drain through intelligent workload placement

NEW YORK and TEL AVIV, Israel, Feb. 25, 2025 /PRNewswire/ -- ScaleOps, the leader in real-time automated cloud resource management, today announced the general availability of their Pod Placement feature, a breakthrough solution that transforms how companies manage their Kubernetes infrastructure. The new capability intelligently organizes 'unevictable' workloads that traditionally couldn't be moved or modified, bringing unprecedented efficiency to enterprise Kubernetes environments.

Modern cloud environments rely on automatic resource optimization to control costs, continuously shifting workloads to maximize efficiency and eliminate waste. However, most enterprise Kubernetes clusters contain workloads with scheduling and eviction constraints that become permanently fixed to specific computing nodes. Developers often place different restrictions on how cloud resources should be allocated and reclaimed for their workloads, creating cost management challenges for DevOps teams and infrastructure owners. Once these unevictable workloads are anchored to nodes, they block resource consolidation and force companies to maintain excess capacity, driving up cloud costs significantly across their infrastructure.

ScaleOps' Pod Placement technology introduces a new approach to this challenge through advanced scheduling algorithms that group unevictable workloads on a common set of nodes. This new capability automatically gains insight into the workload's application-specific context, examining workload requirements and system constraints to optimize placement while maintaining critical service safeguards. Unlike traditional approaches that require complex configuration changes, Pod Placement works seamlessly alongside existing Kubernetes schedulers and cluster autoscalers, delivering up to 50% in additional cost savings beyond standard optimization methods.

"After deploying to over a thousand production clusters, the new Pod Placement capability automatically identified that more than 40% contained unevictable workloads creating systemic inefficiencies that traditional optimization tools simply couldn't address," said Guy Baron, co-founder and CTO of ScaleOps. "In many cases, we found that strict pod disruption budgets were responsible for nearly 50% more resources than necessary because the cluster autoscaler couldn't consolidate the nodes effectively. Pod Placement is the first solution to solve this at the scheduling layer, working in harmony with existing Kubernetes components while delivering immediate value. Early adopters have seen their node counts drop significantly while maintaining strict availability requirements."

The platform's zero-touch deployment requires no changes to existing workloads, pod specifications, or DevOps processes. Pod Placement continuously and automatically optimizes workload distribution, while intuitive dashboards allow teams to monitor the ongoing cost savings and efficiency gains in real-time. The solution is available immediately for all ScaleOps customers.

About ScaleOps
ScaleOps is the leading automated cloud resource management platform, addressing the critical challenge of efficient resource allocation in cloud-native environments. Through its container-level automation platform, ScaleOps eliminates the operational friction between application teams and DevOps by dynamically adjusting resources in real time. The company's AI-powered automatic scaling and intelligent workload placement capabilities are used and trusted by industry leaders, including John Deere, Northwestern Mutual, Wiz, Affirm, SentinelOne, Akamai, ShareChat, and additional industry leaders and Fortune 100 companies. ScaleOps enables enterprises to optimize cloud infrastructure while maintaining peak application performance. With $80M in total funding, ScaleOps continues to advance cloud resource optimization technology.