Startups > ScaleOps

ScaleOps

ScaleOps autonomously optimizes Kubernetes and AI infrastructure resources in real-time.

Series C $210M total Founded 2022 New York, New York 129 employees

ScaleOps is an autonomous platform that continuously monitors and optimizes Kubernetes and AI infrastructure resources in real-time, dynamically adjusting CPU, memory, GPU, and replica counts based on live workload behavior. It eliminates manual configuration and operates with full context awareness to prevent performance issues and downtime that plague other automation tools. Customers typically achieve up to 80% cost reduction while improving performance and stability across their infrastructure.

Problem solved

Engineering teams waste significant cloud resources on overprovisioned Kubernetes clusters and lack autonomous optimization that maintains both performance and cost efficiency without manual intervention.

Target customer

Enterprise engineering teams running Kubernetes clusters with significant GPU and compute workloads, particularly Series B+ companies with 500+ employees in cloud-native environments (GCP, AWS, Azure).

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Founders

Yodar Shafrir

CEO & Co-Founder

Former Software Team Lead for GPU orchestration at Run:ai (acquired by Nvidia); 15-year professional triathlete for Israel; IDF Officer 2010-2014; previously worked at Fireglass (acquired by Symantec) and Mellanox Technologies.

Guy Baron

CTO & Co-Founder

Co-founder and technical leader at ScaleOps; limited public background information available.

Funding history

Series C $130M March 2026 Led by Insight Partners · Lightspeed Venture Partners, NFX, Glilot Capital Partners, Picture Capital

Series B $58M November 2024 Led by Unknown · Unknown

Series A Unknown 2023 Led by Lightspeed Venture Partners · Unknown

Seed Not disclosed Unknown Led by Unknown · Unknown

Total raised: $210M

Industries

"New York New York" AI Infrastructure Cloud Computing Software Engineering DevOps

Pricing

Custom enterprise pricing based on environment-specific factors; uses standard subscription model with costs dependent on existing pod overprovisioning levels. Contact required for accurate pricing.

Notable customers

Adobe, Wiz, DocuSign, Salesforce, Coupa, Booksy, Dazz, Outbrain, Orca Security

Integrations

Kubernetes, AWS, Google Cloud Platform (GCP), Microsoft Azure, GPU infrastructure

Tech stack

Open Graph HTTP/3 WordPress (Blogs) HubSpot Analytics (Analytics) Google Analytics (Analytics) Cloudflare Bot Management (Security) Google Font API (Font scripts) WP Rocket (Caching) PHP (Programming languages) Google Workspace (Email) Cloudflare (CDN) HubSpot (Marketing automation) MySQL (Databases) Elementor (Page builders) Yoast SEO Premium (SEO) Yoast SEO (SEO) WP Engine (PaaS) Calendly (Appointment scheduling) HubSpot WordPress plugin (WordPress plugins) Contact Form 7 (WordPress plugins) GoDaddy (Hosting)

Website

scaleops.com/

Competitors

Cast AI

Operates at the node and infrastructure layer optimizing instance types and Spot usage, while ScaleOps focuses on Kubernetes pod-level resource optimization within clusters.

Platform9

Broader Kubernetes platform, while ScaleOps specializes in autonomous resource optimization and GPU workload management.

Solo.io

Focuses on API and service mesh infrastructure, whereas ScaleOps targets compute resource optimization and scaling automation.

Kubecost

Emphasizes cost visibility and governance, while ScaleOps provides autonomous real-time optimization with continuous monitoring.

Why this matters: ScaleOps has achieved remarkable scale in a competitive space ($210M raised, $800M valuation) by pioneering truly autonomous infrastructure optimization that reduces costs 80% while maintaining or improving performance—a claim few infrastructure tools can substantiate with real customer evidence. Its 450% YoY growth and GPU workload focus position it as a key player in the expanding AI infrastructure optimization market.

Best for: Enterprise teams running production Kubernetes clusters who need autonomous resource optimization without manual configuration, especially those managing GPU-intensive AI workloads at scale.

Use cases

Reducing cloud infrastructure costs without performance degradation

Booksy reduced CPU requests by ~80% while maintaining application performance by deploying ScaleOps' autonomous optimization. The platform continuously monitors workload demand patterns and automatically right-sizes resources, eliminating the manual process of capacity planning that typically requires engineering effort and carries risk of misconfiguration.

Scaling AI and GPU workloads efficiently

Dazz nearly doubled running workloads and pods while maintaining the same operational costs using ScaleOps' AI infrastructure module. The platform provides real-time GPU visibility and optimization, automatically managing replica counts and instance selection to maximize throughput without proportional cost increases, critical for data-intensive ML pipelines.

Achieving high automation rates in large environments

Outbrain automated over 90% of its workloads across Azure with ScaleOps, reducing manual intervention from engineering teams. The autonomous, context-aware approach eliminates the downtime and performance issues that come from partial automation, allowing teams to focus on product development instead of infrastructure management.

Alternatives

Cast AI Choose Cast AI if you need broad infrastructure-layer optimization across multiple cloud providers including instance reshaping; choose ScaleOps if you want pod-level optimization and GPU management within your existing Kubernetes clusters.

Kubecost Choose Kubecost for detailed cost visibility and governance controls; choose ScaleOps if you want autonomous optimization that actively reduces costs without manual intervention.

Platform9 Choose Platform9 for comprehensive Kubernetes management across hybrid environments; choose ScaleOps for specialized autonomous resource optimization focused on performance and cost efficiency.

FAQ

What does ScaleOps do? +

ScaleOps is an autonomous platform that continuously monitors Kubernetes and AI infrastructure, automatically optimizing CPU, memory, GPU, and replica counts in real-time based on live workload behavior. It operates with full context awareness to prevent performance degradation that can occur with partial automation, eliminating the need for manual configuration while delivering up to 80% cost reductions alongside improved stability.

How much does ScaleOps cost? +

ScaleOps uses custom enterprise pricing based on environment-specific factors and the extent of pod overprovisioning in your clusters. The company uses a standard subscription model. Contact their sales team for accurate pricing tailored to your infrastructure.

What are alternatives to ScaleOps? +

Key alternatives include Cast AI (broader infrastructure-layer optimization), Kubecost (cost visibility and governance), and Platform9 (comprehensive Kubernetes management). Each differs in scope: Cast AI focuses on instance optimization, Kubecost on visibility, and Platform9 on broader platform capabilities, while ScaleOps specializes in autonomous pod-level and GPU optimization.

Who uses ScaleOps? +

ScaleOps serves enterprise engineering teams running production Kubernetes clusters, including Adobe, Wiz, DocuSign, Salesforce, and Coupa. Typical customers are Series B+ companies with 500+ employees managing significant cloud infrastructure across GCP, AWS, or Azure, particularly those running AI and GPU-intensive workloads.

How does ScaleOps compare to Cast AI? +

ScaleOps operates at the Kubernetes pod and GPU level, optimizing resource requests and replica counts within clusters without requiring infrastructure changes. Cast AI operates at the node and infrastructure layer, replacing native autoscaling and reshaping instance types and sizes. ScaleOps emphasizes autonomy and context-awareness; Cast AI offers broader cross-cloud infrastructure optimization.