ScaleOps vs Extend
Side-by-side comparison of AI visibility scores, market position, and capabilities
Raised $130M Series C at $800M valuation in March 2026; customers including Adobe, Salesforce, DocuSign, and Wiz report up to 80% Kubernetes cloud cost reductions via autonomous real-time resource optimization.
About
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.
San Francisco AI document processing using LLMs for enterprise data extraction from invoices, contracts, and forms; $17M Innovation Endeavors and YC-backed at multi-million ARR serving Brex and Square cash-flow positive.
About
Extend is a San Francisco-based AI document processing platform using large language models to provide accurate data extraction and document understanding for enterprise workflows — turning unstructured documents (invoices, contracts, medical records, financial statements, onboarding forms) into structured data at the accuracy and cost level that manual processing and traditional OCR cannot match at scale. Backed with $17 million raised in combined seed and Series A funding led by Innovation Endeavors with Y Combinator, Homebrew, and angel investors including Adobe's CSO and Vercel's CEO, Extend reached multi-million dollar ARR and cash-flow positive status serving customers including Brex, Square, Checkr, and multiple Fortune 500 companies.
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