ScaleOps GPU Platform

ScaleOps GPU Platform is a GPU optimization solution for Kubernetes-based AI infrastructure. It is designed for organizations running production AI workloads, especially inference workloads, LLM serving, batch AI jobs, and agentic applications that depend on expensive GPU capacity.

Modern AI infrastructure creates a major resource management challenge. GPUs are often the highest-cost resource in the environment, but Kubernetes typically treats GPUs as whole units. This means workloads often request or receive more GPU capacity than they actually consume. For inference workloads, where usage may be bursty, idle, memory-heavy, or latency-sensitive, this can create significant underutilization and unnecessary spend. Internal ScaleOps positioning notes that Kubernetes cannot natively request fractional GPUs and lacks visibility into actual GPU compute versus memory consumption, which leads to waste in inference environments.

ScaleOps GPU Platform addresses this by adding an automated optimization layer for GPU workloads. The product monitors actual GPU compute and memory consumption separately, dynamically right-sizes workloads, improves workload placement, and rebalances resources automatically. This helps eliminate manual capacity planning and allows infrastructure teams to get more value from the GPU capacity they already operate.

The product is especially aligned to production inference workloads. Internal product positioning states that ScaleOps focuses on inference because these workloads are production-facing, customer-serving, and closely aligned with ScaleOps' production infrastructure expertise. AI Infra also supports LLM optimization use cases, including right-sizing vLLM memory consumption and enabling GPU sharing for LLM workloads.

For agentic AI workloads, ScaleOps GPU Platform helps manage bursty memory patterns and reliability-sensitive infrastructure behavior. Internal notes describe agentic workloads as driving more microservice calls, bursty memory consumption, and costly LLM reruns when reliability failures occur. ScaleOps' agentic workload policies are positioned around reliability-focused optimization, fewer crashes, and automatic handling of bursty memory behavior.

In practice, ScaleOps GPU Platform helps platform, DevOps, ML infrastructure, and cloud engineering teams increase GPU utilization, reduce infrastructure waste, simplify operations, and support the growth of self-hosted AI workloads. Internal launch messaging also emphasizes fast installation and value from day one.