While AI companies burn billions on GPU clusters that sit idle 40-70% of the time, a 31-person team in Tel Aviv is quietly solving the most expensive inefficiency in enterprise AI. NVIDIA didn't acquire them for $700M by accident.

"The real AI infrastructure problem isn't model complexity—it's that your $400K GPU cluster is running at 30% utilization while you wait for the next training job."

The company's 53% direct traffic is telling—this isn't a product that needs SEO gimmicks. Engineers and ML platform teams bookmark Run:ai because it solves a painful, measurable problem: turning GPU CAPEX into revenue-generating capacity. With 16.9K backlinks from technical blogs and DevOps forums, they've built organic credibility in the exact communities that control infrastructure budgets.

• Dynamic GPU partitioning: Split physical GPUs into multiple virtual instances without performance degradation
• Priority-aware scheduling: Critical inference jobs preempt lower-priority training workloads automatically
• Cost attribution: Every team sees their exact GPU consumption, eliminating shadow IT and over-provisioning
• Kubernetes-native: Deploys in hours, not weeks, integrating with existing MLOps stacks