ScaleOps is an AI infrastructure company that autonomously optimizes cloud, GPU, CPU, and memory resources, helping enterprises run AI applications and models more efficiently while reducing infrastructure costs.
You co-founded ScaleOps in 2022, just as generative AI was beginning to take off. What inspired the company, and did you anticipate how quickly AI infrastructure would become such a major challenge?
Before founding ScaleOps, I led engineering at Run:AI, where we helped companies maximize GPU utilization for AI training. Through working closely with customers, I saw that while training was well addressed, inference—the production use of AI models—was rapidly becoming the next major challenge. Companies struggled to efficiently manage GPUs, CPUs, and memory in production, preventing them from getting the most out of their infrastructure.
The problem wasn't only technical but organizational. Platform engineering teams owned the infrastructure, while application developers, data scientists, and AI engineers focused on building models and agents without visibility into infrastructure costs. Existing tools highlighted inefficiencies but didn't solve them. That inspired us to build ScaleOps—an autonomous platform that continuously manages cloud resources according to the needs of every application, model, and agent, allowing engineering teams to focus on innovation instead of infrastructure management.
Many companies remain skeptical about AI ROI, yet ScaleOps claims to reduce AI infrastructure costs significantly. How do you demonstrate that value to customers?
Today, most organizations are racing to become AI-first, prioritizing speed over efficiency. As a result, many deploy frontier models that are far more powerful—and expensive—than their workloads actually require. We help customers evaluate those workloads and transition appropriate use cases to open-source models that can run on their own infrastructure much more efficiently.
Once those models are deployed, we optimize GPU utilization and infrastructure automatically. That combination can make AI workloads more than 15 to 20 times more efficient. Instead of simply reducing costs, we help organizations build sustainable AI infrastructure that can scale as adoption grows.
The AI infrastructure market is becoming increasingly competitive. What differentiates ScaleOps from other providers?
Our platform understands the context of every application, model, and AI agent. Rather than relying on predefined rules, we determine what each workload needs at any given moment and autonomously manage the underlying infrastructure accordingly. Every decision is made automatically based on the application's purpose and real-time resource requirements.
Most competing solutions still require users to configure policies, tune infrastructure, and continuously manage resources themselves. ScaleOps removes that burden entirely. There is no complex implementation or ongoing manual tuning—the platform makes those decisions automatically so engineering teams can spend their time building products instead of managing infrastructure.
You're using AI to manage the infrastructure that AI itself depends on. Does that introduce another layer of risk?
Our automation is intentionally deterministic. Rather than making unpredictable decisions, it uses all the available context about applications, models, and infrastructure to determine the best way to allocate resources. For every model, decisions such as which GPU to use, how many GPUs are required, and how infrastructure should scale are based on defined operational context.
Today, those decisions are largely made manually by engineers. We simply automate them using the full understanding of each workload. Instead of adding uncertainty, we remove repetitive operational work while ensuring infrastructure continuously matches the needs of AI applications.
Which types of organizations are you focused on today, and how do you view the size of the long-term market opportunity?
Our market is essentially every enterprise running modern software infrastructure. Any Global 2000 company deploying AI applications, agents, or models must also manage the infrastructure that supports them, making them a potential customer.
As AI adoption expands across industries, the demand for autonomous infrastructure management will continue growing. Regardless of sector, organizations increasingly need to balance performance, reliability, and cost while running complex AI workloads at scale.
What do you see as the biggest infrastructure challenge organizations will face as AI adoption accelerates?
The biggest constraint will be compute. AI makes it possible to build almost anything, but organizations still have to determine how to allocate CPUs, memory, and GPUs efficiently across increasingly complex workloads.
Agentic AI introduces multiple interconnected agents communicating with one another, dramatically increasing infrastructure demands.
Managing those dynamic workloads manually simply doesn't scale. Our role is to translate the business context of AI applications into infrastructure decisions automatically, ensuring compute resources are continuously optimized as demand changes. We believe infrastructure management will become one of the defining bottlenecks of enterprise AI.
Open-source AI models are becoming increasingly popular. How is that changing the way customers approach AI deployment?
We're seeing many organizations move toward open-source models, partly because they're more cost-effective, but also because they allow companies to keep sensitive data within their own infrastructure rather than relying on external environments. For many enterprises, data control has become just as important as cost.
Managing open-source models and GPU fleets, however, creates significant operational complexity. ScaleOps automates that entire process, managing both the models and the underlying infrastructure so customers no longer need to perform extensive manual tuning. That allows them to benefit from open models while minimizing operational overhead.
You recently secured a Series C funding round. Given that, what are your priorities for the next 12 months?
Our focus is expanding our AI infrastructure platform with additional products. Today, it's easier than ever for organizations to build and deploy applications, and AI agents are accelerating that trend even further. Every new application creates highly dynamic demand for compute resources that must be managed efficiently.
We're investing heavily in products that autonomously manage both application infrastructure and GPU infrastructure for AI inference. As workloads become increasingly dynamic, organizations need infrastructure that adapts automatically, and that's where we're concentrating our investment.
