Aranya

Aranya is a platform designed to deploy and operate production-ready AI infrastructure, moving from bare metal hardware to live clusters in under 48 hours. It is tailored for organizations with demanding compute needs, such as AI inference and training companies, and ML platforms that require GPU-intensive workloads at scale. The service leverages clusterdOS, an open-source distributed operating system that extends Kubernetes to manage the full production stack, including networking, storage, GPU scheduling, and inference or training tooling.

The platform is aimed at teams where compute is a core product constraint and where building out a dedicated platform engineering team is not desirable. Aranya architects each cluster based on the specific requirements of the workload, specifying hardware and infrastructure needs before deploying and operating the stack. This approach accommodates custom networking, storage, and high-throughput GPU setups—addressing challenges that standard managed Kubernetes services may not handle out of the box.

Aranya emphasizes a GitOps-native workflow, where infrastructure is managed declaratively and versioned in Git, eliminating configuration drift and enabling reproducible environments. ClusterdOS provides self-healing observability and production monitoring from day one, with the ability to toggle applications and features as needed. The system is designed to be accessible without requiring prior Kubernetes experience; users manage clusters primarily through Git operations, with the platform handling the complexities of infrastructure management.

Delivery is focused on fully operated bare metal clusters, with support for multi-location or multi-data center deployments. ClusterdOS, the core technology powering Aranya, is open source, allowing transparency and community collaboration. The platform positions itself as a bridge between AI companies and bare metal infrastructure, offering rapid deployment, hands-on support, and tailored architecture for mission-critical workloads.