ScaleOps operates as a closed-loop optimization engine for Kubernetes environments where static resource configurations fail to keep up with dynamic AI and cloud workloads. The platform observes workload demand in real time, evaluates performance signals across the entire cluster, and executes allocation changes automatically within enterprise-defined policies. This covers pod rightsizing, replica count optimization, node management, spot instance utilization, and increasingly GPU allocation for AI model inference and training workloads.
The GPU optimization capabilities address the defining infrastructure bottleneck of the AI era. ScaleOps dynamically allocates GPUs based on actual demand, applies LLM memory rightsizing to reduce overprovisioning, and optimizes MIG partitioning to minimize waste. Cold start minimization and context switching optimization keep models warm for real-time inference, while HPA optimization scales replicas to match live demand patterns. Combined GPU and LLM metrics observability reveals performance gaps and cost inefficiencies that manual monitoring misses.
Founded in 2022 by Yodar Shafrir, a former engineer at Run:ai (acquired by Nvidia), ScaleOps has raised over 210 million in total funding with a Series C led by Insight Partners and backed by Lightspeed, NFX, and Glilot Capital. The platform is available on AWS, Azure, and Google Cloud marketplaces with FIPS compatibility for FedRAMP environments. Self-hosted deployment supports cloud, on-premises, and air-gapped installations, and the company reports 450 percent year-over-year growth with plans to triple headcount by year end.
