ScaleOps
Procurement partnerAbout
ScaleOps provides an autonomous Kubernetes resource orchestration platform that continuously allocates and scales CPU, GPU, and memory for AI agents, inference models, and applications running in production. The platform automatically rightsizes pods, optimizes nodes, manages replicas, and handles GPU workloads without manual tuning or code changes. It is self-hosted by design and deployable across any Kubernetes environment including EKS, AKS, GKE, OpenShift, on-prem, and air-gapped environment...
Details
Website
scaleops.comOfferings
14 active
VenSearch Intelligence
Ideal Customer
- ScaleOps targets mid-market to large enterprise companies running significant Kubernetes infrastructure, particularly those managing hundreds of clusters or thousands of pods in production.
- Their sweet spot is engineering and platform teams at cloud-native or Fortune 500 enterprises with complex, dynamic workloads across multiple clusters, including organizations deploying AI/ML workloads that require GPU resource management.
Key Differentiators
- ScaleOps differentiates itself by taking fully autonomous, real-time action on Kubernetes resources rather than producing static recommendations that require manual implementation—a key distinction from tools like Kubecost and earlier-generation rightsizers.
- Its self-hosted, in-cluster architecture (installed via a single Helm command) addresses data sovereignty concerns that prevent enterprises from using external SaaS control planes like Cast AI.
Industries Served
- ScaleOps primarily serves technology, cybersecurity, software development, financial technology, and advertising technology companies that operate large-scale Kubernetes infrastructure.
- Documented customer industries include cybersecurity (Orca Security, Wiz, Dazz, Armis, SentinelOne, Cato Networks), fintech (AccessFintech, PayU), ad-tech (Outbrain, Rokt), and enterprise software (Adobe, DocuSign, Salesforce, Coupa, OutSystems).
Service Coverage
- ScaleOps operates globally, with headquarters split between New York City and Tel Aviv, Israel, and a distributed team across North America, Europe, and India.
- The platform is software-based and cloud-agnostic, meaning it can be deployed in any geography where Kubernetes runs, including EKS, AKS, GKE, OpenShift, on-premises, and fully air-gapped environments.
Partnerships
- ScaleOps has a formal partnership with AWS, including listing on the AWS Marketplace, AWS co-sell support, Solutions Architect enablement, and the Amazon EC2 Spot Ready Product validation through the AWS Partner Network.
- The platform natively integrates with AWS EKS and Karpenter, Microsoft AKS, and Google GKE, as well as OpenShift and on-premises environments.
Company Size & Ownership
- ScaleOps was founded in March 2022 by co-founders Yodar Shafrir (CEO) and Guy Baron (CTO).
- As of mid-2026, the company has approximately 120–150 employees, having tripled its headcount over the prior 12 months with plans to triple again by end of 2026.
Compliance & Certifications
- ScaleOps holds SOC 2 Type II certification and is listed as a FinOps Certified Platform (Premier Member of the FinOps Foundation) as well as a CNCF (Cloud Native Computing Foundation) member.
- The platform has also received the Amazon EC2 Spot Ready Product validation from AWS, reflecting technical vetting through the AWS Partner Network.
Compiled from public sources by VenSearch · Last researched October 14, 2026
Offerings
14 offeringsAI SRE Agent
An AI-native tool that connects ScaleOps to agentic workflows, serving as part of the ScaleOps AI suite. It is designed to bring autonomous site reliability engineering capabilities to Kubernetes infrastructure management.
Air-Gapped Deployment
A deployment option for ScaleOps that supports fully isolated, air-gapped Kubernetes environments where no external network connectivity is available. Allows organizations with strict security requirements to run ScaleOps without internet access.
Cost Monitoring
Provides instant visibility into actual Kubernetes costs broken down by cluster, namespace, team, application, annotations, or labels, with native integration to AWS CUR, GCP Billing Export, and Azure Cost Management for accurate provider-matched cost data. Includes a cost comparison dashboard to measure optimization impact, GPU and network cost drill-down by workload, and multi-cluster cost management through a single centralized dashboard.
GPU Platform
Provides autonomous GPU and memory optimization across every accelerator, including automated fractional GPUs, GPU inference observability, GPU replicas optimization, batch inference optimization, and GPU memory optimization. Enables dynamic GPU sharing and automated workload rightsizing so each workload receives resources based on real-time demand.
Java Resource Management
Provides JVM-aware resource optimization for Java workloads on Kubernetes by analyzing live JVM behavior including heap usage, garbage collection patterns, and OOM signals to make accurate real-time resource decisions. Automatically detects and optimizes Java workloads across clusters with no onboarding or configuration required, supporting custom Java applications, Spark executors, and long-running Java services.
Karpenter Optimization
Enhances Karpenter with context-aware instance type optimization, automatically selecting the best EC2 instance families, types, generations, and sizes for workloads using the EC2 Fleet API. Sets optimal disruption budgets to control when and how nodes are consolidated, and provides visibility into configuration gaps, inefficient node pools, and mismatched instance types without replacing Karpenter.
Node Optimization
Continuously consolidates and replaces underutilized nodes with more efficient node types that match the resource needs of running pods, working seamlessly with the existing Kubernetes Cluster Autoscaler. Automatically creates and manages optimized node pools without requiring manual configuration of types or settings, eliminating underutilized capacity.
Pod Rightsizing
Automatically rightsizes CPU and memory resource requests for Kubernetes pods in real time based on live workload behavior and cluster conditions. Supports in-place pod resize, boot-time CPU optimization, automatic pod healing for OOM kills and CPU throttling, and works alongside HPA and KEDA without conflicts. Compatible with GitOps workflows including Argo CD and Flux.
Replicas Optimization
Dynamically manages minimum and maximum replica counts and scaling triggers to cut costs and proactively scale ahead of demand using predictive, application context-aware automation. Works with existing HPA and KEDA definitions and any defined scaling metrics, providing centralized horizontal scaling policy control for DevOps and platform teams.
ScaleOps MCP
An AI-native tool that connects ScaleOps to agentic workflows as part of the ScaleOps AI suite. It enables integration of ScaleOps capabilities into model context protocol-based agentic workflows.
Self-Hosted Deployment
ScaleOps is self-hosted by design and deployable anywhere Kubernetes runs, installed with a single Helm command. It supports deployment across EKS, AKS, GKE, OpenShift, on-premises, and air-gapped environments with full sovereign control over cluster allocation.
Smart Pod Placement
Automatically places unevictable pods such as those with eviction-preventing annotations, local storage, strict PDBs, and ownerless pods onto fewer nodes to improve bin packing and enable the cluster autoscaler to scale down more nodes. Operates alongside the Kubernetes default scheduler, respecting all scheduling constraints including taints, affinities, and anti-affinities with zero-touch onboarding.
Spot Optimization
Intelligently shifts more workloads to Spot instances by optimizing at the pod level, automatically distributing replicas across Spot and On-Demand instances with real-time rebalancing. Provides centralized, policy-driven Spot management across the entire cluster, defining replica ratios between Spot and On-Demand, automatic fallback, and minimum On-Demand capacity.
Workload Troubleshooting
Surfaces real-time signals across Kubernetes clusters for tracking critical performance and reliability issues including OOM kills, CPU throttling, and failed health checks across multiple clusters. Provides a unified timeline of all cluster events including policy changes, restarts, rescheduling, and scaling actions, with alerting routed to Slack and local metrics stores.
Considering ScaleOps?
Our team will work with you to determine if ScaleOps is the right fit for your organization. From research and assessment through procurement negotiation and ongoing vendor management — we handle the entire process at no cost to you.