Run

Run offers a robust platform for AI teams seeking to optimize hardware resources, particularly GPUs, throughout their development and production workflows. By automating and orchestrating complex AI workloads, the solution ensures that precious compute resources are allocated efficiently, enabling teams to run more applications with less idle time and lower costs. This orchestration extends seamlessly across both cloud-based and on-premise environments, giving organizations flexible deployment options based on their infrastructure needs.

The platform stands out with advanced features such as granular GPU sharing, which allows multiple users or processes to utilize portions of a single GPU, and node pooling, which helps manage and prioritize workloads across clusters of varying hardware. This is particularly useful for teams running a mix of research experiments, development environments, and large-scale inference tasks. Full-stack visibility into resource use and team activity means that infrastructure managers and AI leads have actionable insights to make strategic decisions and enforce policies that prevent bottlenecks or resource hogging.

While the rich feature set makes it powerful, Run does require expertise in modern DevOps practices—especially Kubernetes orchestration. As a result, onboarding tends to involve a learning period, and is best suited for organizations with dedicated technical staff. Ultimately, Run empowers AI teams to achieve faster iteration cycles, better cost management, and increased efficiency in multi-user, GPU-intensive environments.

This product is particularly suited for technical leaders and IT managers responsible for maximizing the performance of AI development teams and managing large fleets of GPU resources. Typical users include CTOs, infrastructure engineers, and machine learning operations (MLOps) specialists at organizations with significant investment in AI research or production, such as growing tech companies, research institutions, and enterprises in fields like healthcare or automotive.

Engineering departments running multiple projects on shared GPU clusters will find value in resource scheduling, quota enforcement, and workload orchestration. Academic or research labs using GPU-intensive notebooks, as well as startups trying to optimize limited infrastructure, can also leverage Run to stretch their computational budgets further.

The product is especially useful where teams need to balance fair access, cost control, and high utilization across multiple users and workloads, and where deep integration with containerized cloud-native tools like Kubernetes is a priority.