ScaleOps launches AI infrastructure resource management for self-hosted AI at scale
ScaleOps has introduced an AI Infra Product that extends its cloud resource management platform to self-hosted GenAI models and GPU-based applications. The goal: keep GPU usage high, keep latency low, and stop the budget bleed from idle hardware.
The platform is already running in production for companies like Wiz, DocuSign, Rubrik, Coupa, Alkami, Vantor, Grubhub, Island, Chewy, and multiple Fortune 500 enterprises. Early deployments report 50-70% savings as teams move from manual tuning to continuous automation.
Why this matters to leadership
- GPU spend is spiking while utilization often stays low. Idle capacity quietly inflates cloud bills.
- Large models load slowly and struggle during demand spikes, pushing teams to overprovision "just in case."
- Engineers lose time on constant tuning, throttling, and capacity shuffling instead of shipping features.
What the AI Infra Product does
- Allocates and scales GPU resources in real time based on live demand.
- Increases utilization while keeping performance steady during spikes.
- Accelerates model load and warm-up to reduce latency and cold-start issues.
- Uses application context-awareness with continuous automation, so teams spend less time babysitting workloads.
In short, it helps AIOps and DevOps teams run self-hosted LLMs and AI services efficiently, without overprovisioning or manual tweaks.
Executive perspective
"Cloud-native AI infrastructure is reaching a breaking point," said Yodar Shafrir, CEO and Co-Founder of ScaleOps. "Cloud-native architectures unlocked great flexibility and control, but they also introduced a new level of complexity. Managing GPU resources at scale has become chaotic - waste, performance issues, and skyrocketing costs are now the norm. The ScaleOps platform was built to fix this. It delivers the complete solution for managing and optimizing GPU resources in cloud-native environments, enabling enterprises to run LLMs and AI applications efficiently, cost-effectively, and while improving performance."
What leaders can do next
- Ask for a baseline: current GPU utilization, queue times, model load times, and spend per request.
- Run a time-boxed pilot on one or two high-traffic models; measure utilization, latency, and dollar savings week over week.
- Set clear SLOs (latency, availability) and tie scaling policies to them instead of guesswork.
- Review governance: tagging, cost allocation, and alerting so Finance can see savings in real numbers, not anecdotes.
- Upskill teams on AI operations, observability, and cost accountability.
Results to expect
- Higher GPU utilization without performance tradeoffs.
- Lower spend via right-sizing and on-demand scaling.
- Faster response times through smarter model loading.
- Less engineering time spent on manual resource tweaks.
Learn more about the product and request a demo at scaleops.com/ai.
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