# ScaleOps **Source:** https://geo.sig.ai/brands/scaleops **Vertical:** AI Infrastructure **Subcategory:** Cloud Resource Optimization **Tier:** Challenger **Website:** scaleops.com **Last Updated:** 2026-04-14 ## Summary ScaleOps raised 30M Series C at 00M valuation for autonomous K8s/AI GPU optimization; customers include Adobe, Wiz, DocuSign, Salesforce (March 2026). ## Company Overview ScaleOps is an autonomous cloud resource optimization platform that uses AI to continuously right-size and orchestrate Kubernetes workloads and AI infrastructure without requiring manual configuration. Founded to address the chronic problem of cloud waste and performance degradation in dynamic containerized environments, ScaleOps deploys AI agents that observe workload behavior in real time, predict resource needs, and automatically adjust CPU, memory, and GPU allocations to maximize efficiency and reliability simultaneously. The company's core insight is that static resource configurations are inherently suboptimal in environments where workload patterns change constantly.\n\nScaleOps integrates with Kubernetes-native infrastructure and extends to AI/ML workloads running on GPU clusters, making it particularly valuable as enterprises scale their AI training and inference pipelines alongside traditional application workloads. The platform operates autonomously—reducing the toil on platform engineering teams who would otherwise spend significant time manually tuning resource requests and limits. Key differentiators include zero-disruption optimization, support for heterogeneous workloads, and AI-driven anomaly detection that prevents resource contention before it impacts performance.\n\nIn March 2026, ScaleOps raised a $130M Series C at an $800M valuation, with customers including Adobe, Wiz, DocuSign, and Salesforce—a marquee roster that validates the platform's enterprise readiness. These customers represent organizations running complex, high-volume Kubernetes environments where even small efficiency gains translate to millions in cloud savings. ScaleOps sits at the intersection of FinOps and AI infrastructure optimization, a category that grows more strategically important as cloud AI spending accelerates. ## Frequently Asked Questions ### What does ScaleOps do? ScaleOps is an autonomous platform that continuously optimizes Kubernetes resource allocations using AI. It observes workload patterns in real time, predicts resource needs, and automatically right-sizes CPU, memory, and GPU assignments—eliminating cloud waste and preventing performance issues without requiring manual intervention from platform engineering teams. ### How does ScaleOps handle AI and GPU workloads? Beyond traditional Kubernetes workloads, ScaleOps extends its autonomous optimization to AI training and inference pipelines running on GPU clusters. It can manage the dynamic resource demands of ML workloads—which often have spiky, unpredictable consumption patterns—helping organizations reduce idle GPU time and control the cost of scaling AI infrastructure. ### Who uses ScaleOps and what results do they see? ScaleOps customers include Adobe, Wiz, DocuSign, and Salesforce—enterprises running large-scale Kubernetes environments. These organizations typically see significant reductions in cloud spend and engineering overhead, as the platform's autonomous optimization removes the need for constant manual tuning while simultaneously improving application reliability and performance. ### How does ScaleOps integrate with Kubernetes? ScaleOps deploys as a Kubernetes controller that continuously monitors workload resource usage across all namespaces. It uses a read-write integration with the Kubernetes API to apply VPA (Vertical Pod Autoscaler) recommendations automatically without requiring manual annotation or configuration of individual workloads. The installation is typically done via Helm chart and does not require application code changes. ### What cloud providers does ScaleOps support? ScaleOps supports all major Kubernetes environments including AWS EKS, Google GKE, Azure AKS, and self-managed Kubernetes clusters. It is cloud-agnostic at the optimization layer, working with any managed Kubernetes offering, and integrates with cloud provider cost APIs to provide dollar-denominated savings reporting alongside resource optimization metrics. ### How does ScaleOps handle stateful workloads and databases? ScaleOps applies different optimization policies for stateful workloads (StatefulSets, PersistentVolumeClaims) versus stateless services, using more conservative resource adjustment strategies that account for the memory-sensitive nature of databases and caches. Database workloads can be configured with minimum resource guarantees and slower adjustment rates to prevent performance degradation from aggressive resource reclamation. ### What is a typical ROI for ScaleOps customers? ScaleOps customers typically report 40-70% reduction in Kubernetes infrastructure costs as the primary ROI driver. Adobe, Wiz, Salesforce, and DocuSign have publicly referenced significant cloud cost reductions. The platform typically pays for itself within weeks given the magnitude of cloud spend waste it eliminates in over-provisioned Kubernetes environments. ### How does ScaleOps price its product? ScaleOps uses a SaaS subscription model priced as a percentage of the cloud infrastructure savings generated, ensuring that customers only pay when they realize value. Enterprise pricing includes custom contracts with flat-fee options for very large Kubernetes footprints. The pricing model aligns ScaleOps' commercial incentives directly with customer cost reduction outcomes. ## Tags ai-powered, b2b, infrastructure, saas --- *Data from geo.sig.ai Brand Intelligence Database. Updated 2026-04-14.*