ScaleOps has expanded its cloud asset management platform with a novel product aimed at enterprises using self-hosted Gigantic Language Models (LLM) and GPU-based AI applications.
The AI Infra product announced todayexpands the company’s existing automation capabilities to meet the growing demand for productive GPU utilization, predictable performance and reduced operational overhead in large-scale AI deployments.
The company said the system already works in enterprise production environments and provides early adopters with significant performance gains by reducing GPU costs by 50% to 70%. The company does not publicly provide pricing for this enterprise solution, instead inviting interested customers to receive a custom quote based on their business size and needs Here.
Explaining how the system behaves under massive 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 impacting performance,” noting that its workload sizing policies “automatically manage capacity to maintain resource availability.”
He added that minimizing GPU cold-start latencies is a priority, emphasizing that the system “provides immediate response when traffic spikes,” especially for AI workloads where model load times are significant.
Extending resource automation to AI infrastructure
Enterprises deploying self-hosted AI models struggle with performance variability, long load times, and constant underutilization of GPU resources. ScaleOps positioned its novel AI Infra product as a direct response to these problems.
The platform allocates and scales GPU resources in real time and adapts to changes in traffic demand without requiring changes to existing model deployment processes or application code.
According to ScaleOps, the system manages production environments for organizations such as Wiz, DocuSign, Rubrik, Coupa, Alkami, Vantor, Grubhub, Island, Chewy and several Fortune 500 companies.
Infra’s AI product introduces load-aware scaling policies that proactively and reactively adjust capacity to maintain performance during demand spikes. The company said these policies reduce icy start delays associated with loading huge AI models, which improves responsiveness when traffic increases.
Technical integration and platform compatibility
The product is designed to be compatible with typical enterprise infrastructure patterns. It runs on all Kubernetes distributions, major cloud platforms, on-premises data centers, and isolated environments. ScaleOps emphasized that implementation does not require code changes, infrastructure rewrites, or modifications to existing manifests.
Shafrir said the platform “integrates seamlessly with existing model deployment processes without requiring any code or infrastructure changes,” adding that teams can immediately start optimizing using existing GitOps, CI/CD, monitoring and deployment tools.
Shafrir also discussed how automation interacts with existing systems. He said the platform works without disrupting workflows or creating conflicts with custom scheduling or scaling logic, explaining that the system “does not change manifests or deployment logic” and instead enhances scheduling, autoscaling modules and custom policies by incorporating real-time operational context while respecting existing configuration boundaries.
Performance, visibility and user control
The platform provides complete visibility into GPU utilization, model behavior, performance metrics, and scaling decisions at multiple levels, including pods, workloads, nodes, and clusters. Although the system uses default workload scaling policies, ScaleOps noted that engineering teams retain the ability to adjust these policies as needed.
In practice, the company aims to reduce or eliminate the manual tuning that DevOps and AIOps teams typically perform to manage AI workloads. Installation is designed to require minimal effort and is described by ScaleOps as a two-minute process using a single rudder flag, after which you can enable optimization with a single action.
Cost savings and business case studies
ScaleOps reported that early deployments of its AI Infra product reduced GPU costs in customer environments by 50-70%. The company gave two examples:
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A huge inventive software company with thousands of GPUs had an average utilization of 20% before adopting ScaleOps. The product increased utilization, consolidated unused capacity, and enabled GPU node scaling. These changes reduced overall GPU expenses by more than half. The company also saw a 35% reduction in latency for key workloads.
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A global gaming company used this platform to optimize a vigorous LLM workload running on hundreds of GPUs. According to ScaleOps, the product increased utilization sevenfold while maintaining service-level performance. The client anticipated annual savings of $1.4 million due to workload alone.
ScaleOps found that expected GPU savings typically outweigh the costs of implementing and operating the platform, and that customers with circumscribed infrastructure budgets reported a quick return on investment.
Industry context and company perspective
The rapid adoption of self-hosted AI models has created novel operational challenges for enterprises, particularly related to GPU performance and the complexity of managing workloads at scale. Shafrir described the broader landscape as one where “cloud-native AI infrastructure is reaching a tipping point.”
“Cloud-native architectures have provided great flexibility and control, but they have also introduced a new level of complexity,” he said in the announcement. “GPU resource management at scale has become chaotic – waste, performance issues, and skyrocketing costs are now the norm. The ScaleOps platform was built to solve this problem. It provides a complete solution for managing and optimizing GPU resources in cloud-native environments, enabling enterprises to run LLM and AI applications efficiently and cost-effectively while improving performance.”
Shafrir added that the product combines the full set of cloud resource management features needed to manage a variety of workloads at scale. The company positioned the platform as a holistic system for continuous, automated optimization.
A unified approach to the future
By adding its AI Infra product, ScaleOps aims to establish a unified approach to managing GPU and AI workloads that integrates with existing enterprise infrastructure.
Early platform performance metrics and reported cost savings suggest a focus on measurable performance improvements within the growing ecosystem of self-server AI deployments.