Description
🖼️ Tool Name:
Run:ai
🔖 Tool Category:
AI-infrastructure orchestration; it falls under DevOps, CI/CD & Monitoring (Category 103) and Integrations & APIs (Category 44) according to your list.
✏️ What does this tool offer?
Run:ai is a platform that enables enterprises to manage, allocate, and optimise GPU / compute resources for AI workloads. It supports large-scale AI training and inference across cloud, hybrid, and on-premises environments.
It includes AI-native scheduling, dynamic GPU pooling, centralized management of clusters, and API-first architecture for integration with ML ecosystems.
⭐ What does the tool actually deliver based on user experience?
• Centralised management of AI infrastructure: clusters, usage monitoring, capacity planning.
• AI workload orchestration and scheduling: dynamic allocation of GPU resources, efficient job management across users and teams.
• Policy enforcement and governance of resources: role-based access control (RBAC), quotas, fair allocation.
• Integration with Kubernetes, open architecture, hybrid cloud support.
🤖 Does it include automation?
Yes — Run:ai automates key infrastructure workflows: scheduling, resource pooling, monitoring, job distribution and scaling — minimising manual infrastructure management for AI teams.
💰 Pricing Model:
Enterprise / custom pricing (not publicly detailed in full). The product is targeted toward organisations with substantial AI workloads.
🆓 Free Plan Details: Not clearly stated in publicly accessible sources.
💳 Paid Plan Details: Enterprise usage with full features (GPU orchestration, hybrid cloud, governance, high scale) via deployment.
🧭 Access Method:
• Deployable on-premises or in cloud/hybrid environments via Kubernetes clusters.
• API/SDK access for workload submission, resource management, monitoring.
• Contact vendor (e.g., through partners like PNY) for licensing and deployment.
🔗 Experience Link:
https://pages.run.ai