Run – Optimize GPU utilization and orchestrate AI infrastructure

Run is an AI infrastructure optimization platform designed to maximize the efficiency of computational resources, particularly GPUs, for AI development and deployment. It provides organizations with the tools to manage complex AI workloads, streamline operations, and gain full visibility into their infrastructure. By focusing on dynamic resource management, Run helps teams accelerate their AI research and development cycles while controlling costs.

The platform is built for technical teams managing large-scale AI initiatives across cloud and on-premise environments. Its core value lies in transforming expensive, underutilized GPU clusters into highly efficient, shared resources that can handle more concurrent workloads. For teams working with advanced AI agents and automation, this orchestration layer is critical for maintaining productivity and scalability.

What is Run?

Run is a specialized orchestration tool that manages and optimizes AI workloads across GPU clusters. It acts as a layer between your AI applications and the underlying Kubernetes infrastructure, intelligently scheduling jobs, pooling resources, and providing granular control over compute allocation. The primary goal is to eliminate GPU idle time and ensure that expensive hardware is used to its full potential.

Unlike generic cloud management tools, Run is purpose-built for the unique demands of AI pipelines, which often involve iterative experimentation, fluctuating resource needs, and a mix of training and inference tasks. It provides the governance and visibility needed for organizations to scale their AI workflows efficiently across multiple teams and projects.

Key Features

• AI Workload Scheduler: Dynamically schedules and prioritizes AI jobs across the entire lifecycle, from development to production.

• GPU Fractioning & Pooling: Allows multiple workloads to share a single GPU, dramatically improving utilization and cost-efficiency.

• Node Pool Management: Organizes heterogeneous clusters (different GPU types) into logical pools with configurable quotas and policies.

• Fair-Share Scheduling & Quotas: Implements governance controls to ensure equitable resource distribution among teams and projects.

• Unified Visibility Dashboard: Provides a single pane of glass for monitoring infrastructure health, workload status, and resource consumption across environments.

• Customizable Workspaces: Enables users to launch pre-configured, containerized development environments with their preferred tools and frameworks.

Use Cases

• Managing shared GPU clusters for AI research teams across multiple universities or departments.

• Orchestrating large-scale training jobs for foundation models or computer vision systems in tech enterprises.

• Running high-throughput inference pipelines for real-time applications like autonomous vehicle perception or medical image analysis.

• Providing governed, self-service AI development environments for data scientists and ML engineers within a large organization.

• Optimizing cloud spend for AI workloads by maximizing GPU utilization and reducing the need for over-provisioning.

Underlying AI Models or Technology

Run itself is not an AI model but an orchestration platform for infrastructure that runs AI models. Its technology is built on Kubernetes, leveraging and extending its native scheduling capabilities to be AI-aware. The platform's intelligence comes from its schedulers and controllers that understand the resource profiles of different AI workloads, such as the memory bandwidth needs of large language models or the parallel compute requirements of computer vision training tasks.

The core innovation lies in its ability to perform "GPU fractioning," which involves time-slicing and memory partitioning on physical GPUs. This allows multiple containerized workloads to co-locate on a single GPU safely and efficiently, a capability that standard Kubernetes lacks. Run's technology stack is designed to be agnostic to the specific AI frameworks (like PyTorch or TensorFlow) and models running on top of it.

Pricing

Run operates on a custom pricing model. Pricing is not publicly listed and is determined based on the scale of deployment, the number of GPUs or nodes managed, and the specific feature set required by the organization. Interested parties must contact the Run sales team for a quote tailored to their infrastructure and use case.

Pros and Cons

Pros

• Significantly increases GPU utilization, potentially allowing 5-10x more workloads on the same hardware.

• Provides granular governance and quota management for shared AI infrastructure across large teams.

• Offers deep visibility into resource consumption and workload performance, aiding in capacity planning and cost allocation.

• Built on Kubernetes, ensuring compatibility with cloud-native tooling and practices.

Cons

• Requires existing Kubernetes expertise and infrastructure, creating a barrier to entry.

• Involves a complex initial setup and configuration process tailored to specific cluster architectures.

• Lack of transparent, upfront pricing requires a sales engagement to evaluate cost.

Alternatives

Organizations seeking similar infrastructure optimization capabilities may also consider the following platforms. For a broader view of tools in this space, explore our research and discovery collection.

• Kubernetes with Custom Schedulers: Using native Kubernetes with community or in-house developed schedulers like Kueue. This offers maximum flexibility but requires significant engineering investment.

• Managed ML Platforms: Cloud provider services like Amazon SageMaker, Google Vertex AI, or Azure Machine Learning. These offer integrated tooling but can lead to vendor lock-in and may have less granular control over underlying infrastructure.

• Open Source Orchestrators: Platforms like Kubeflow or MLflow, which focus more on the ML pipeline lifecycle than on low-level GPU resource optimization.