ScaleOps has expanded its cloud resource management platform with a brand new product geared toward enterprises operating self-hosted large language models (LLMs) and GPU-based AI applications.
The AI Infra Product announced today, extends the corporate’s existing automation capabilities to handle a growing need for efficient GPU utilization, predictable performance, and reduced operational burden in large-scale AI deployments.
The corporate 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%, in keeping with the corporate. The corporate doesn’t publicly list enterprise pricing for this solution and as a substitute invites interested customers to receive a custom quote based on their operation size and wishes here.
In explaining how the system behaves under heavy load, Yodar Shafrir, CEO and Co-Founding father 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 “robotically manage capability to maintain resources available.”
He added that minimizing GPU cold-start delays was a priority, emphasizing that the system “ensures fast response when traffic surges,” particularly for AI workloads where model load times are substantial.
Expanding Resource Automation to AI Infrastructure
Enterprises deploying self-hosted AI models face performance variability, long load times, and chronic underutilization of GPU resources. ScaleOps positioned the brand new AI Infra Product as a direct response to those 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.
In accordance with ScaleOps, the system manages production environments for organizations including Wiz, DocuSign, Rubrik, Coupa, Alkami, Vantor, Grubhub, Island, Chewy, and several other Fortune 500 firms.
The AI Infra Product introduces workload-aware scaling policies that proactively and reactively adjust capability to take care of performance during demand spikes. The corporate stated that these policies reduce the cold-start delays related to loading large AI models, which improves responsiveness when traffic increases.
Technical Integration and Platform Compatibility
The product is designed for compatibility with common enterprise infrastructure patterns. It really works across all Kubernetes distributions, major cloud platforms, on-premises data centers, and air-gapped environments. ScaleOps emphasized that deployment doesn’t require code changes, infrastructure rewrites, or modifications to existing manifests.
Shafrir said the platform “integrates seamlessly into existing model deployment pipelines without requiring any code or infrastructure changes,” and he added that teams can begin optimizing immediately with their existing GitOps, CI/CD, monitoring, and deployment tooling.
Shafrir also addressed how the automation interacts with existing systems. He said the platform operates without disrupting workflows or creating conflicts with custom scheduling or scaling logic, explaining that the system “doesn’t change manifests or deployment logic” and as a substitute enhances schedulers, autoscalers, and custom policies by incorporating real-time operational context while respecting existing configuration boundaries.
Performance, Visibility, and User Control
The platform provides full visibility into GPU utilization, model behavior, performance metrics, and scaling decisions at multiple levels, including pods, workloads, nodes, and clusters. While the system applies default workload scaling policies, ScaleOps noted that engineering teams retain the flexibility to tune these policies as needed.
In practice, the corporate goals to scale back or eliminate the manual tuning that DevOps and AIOps teams typically perform to administer AI workloads. Installation is meant to require minimal effort, described by ScaleOps as a two-minute process using a single helm flag, after which optimization could be enabled through a single motion.
Cost Savings and Enterprise Case Studies
ScaleOps reported that early deployments of the AI Infra Product have achieved GPU cost reductions of fifty–70% in customer environments. The corporate cited two examples:
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A significant creative software company operating 1000’s of GPUs averaged 20% utilization before adopting ScaleOps. The product increased utilization, consolidated underused capability, and enabled GPU nodes to scale down. These changes reduced overall GPU spending by greater than half. The corporate also reported a 35% reduction in latency for key workloads.
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A worldwide gaming company used the platform to optimize a dynamic LLM workload running on lots of of GPUs. In accordance with ScaleOps, the product increased utilization by an element of seven while maintaining service-level performance. The shopper projected $1.4 million in annual savings from this workload alone.
ScaleOps stated that the expected GPU savings typically outweigh the price of adopting and operating the platform, and that customers with limited infrastructure budgets have reported fast returns on investment.
Industry Context and Company Perspective
The rapid adoption of self-hosted AI models has created recent operational challenges for enterprises, particularly around GPU efficiency and the complexity of managing large-scale workloads. Shafrir described the broader landscape as one wherein “cloud-native AI infrastructure is reaching a breaking point.”
“Cloud-native architectures unlocked great flexibility and control, but in addition they introduced a brand new level of complexity,” he said within the announcement. “Managing GPU resources at scale has develop into chaotic—waste, performance issues, and skyrocketing costs are actually the norm. The ScaleOps platform was built to repair this. It delivers the entire 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.”
Shafrir added that the product brings together the total set of cloud resource management functions needed to administer diverse workloads at scale. The corporate positioned the platform as a holistic system for continuous, automated optimization.
A Unified Approach for the Future
With the addition of the AI Infra Product, ScaleOps goals to determine a unified approach to GPU and AI workload management that integrates with existing enterprise infrastructure.
The platform’s early performance metrics and reported cost savings suggest a deal with measurable efficiency improvements inside the expanding ecosystem of self-hosted AI deployments.
