ScaleOps has expanded its cloud resource management platform with a new product aimed at enterprises operating self-hosted large language models (LLMs) and GPU-based AI applications. The AI Infra Product announced today, extends the company’s existing automation capabilities to address a growing need for efficient GPU utilization, predictable performance, and reduced operational burden in large-scale AI deployments. The company said the system is already running in enterprise production environments and delivering major efficiency gains for early adopters, reducing GPU costs by between 50% and 70%, according to the company. The company does not publicly list enterprise pricing for this solution and instead invites interested customers to receive a custom quote based on their operation size and needs here.In explaining how the system behaves under heavy load, Yodar Shafrir, CEO and Co-Founder of ScaleOps, said in an email to VentureBeat that the platform uses “proactive and reactive mechanisms to handle sudden spikes without performance impact,” noting that its workload rightsizing policies “automatically manage capacity to keep resources available.” He added that minimizing GPU cold-start delays was a priority, emphasizing that the system “ensures instant response when traffic surges,” particularly for AI workloads where model load times are substantial.Expanding Resource Automation to AI InfrastructureEnterprises deploying self-hosted AI models face performance variability, long load times, and persistent underutilization of GPU resources. ScaleOps positioned the new AI Infra Product as a direct response to these issues. The platform allocates and scales GPU resources in real time and adapts to changes in traffic demand without requiring alterations to existing model deployment pipelines or application code.According to ScaleOps, the system manages production environments for organizations including Wiz, DocuSign, Rubrik, Coupa, Alkami, Vantor, Grubhub, Island, Chewy, and several Fortune 500 companies. The AI Infra Product …
