OpenNebula is an open source cloud management platform, licensed under Apache-2.0, for building and operating private clouds, edge infrastructures, distributed cloud deployments, and AI Factories.

What it is

OpenNebula is a cloud management platform that provides a unified way to manage virtual machines, Kubernetes clusters, and GPU-accelerated workloads across on-premises, edge, and hybrid environments. It is organized around a simple cloud operating model, and it exposes a single control surface for virtualized, containerized, and AI workloads rather than leaving each of those to a separate tool. The project is maintained in the open and developed in collaboration with a global community of users and contributors.

The concrete problem it solves is the fragmentation of enterprise infrastructure. Operators who run virtualization, containers, and AI workloads today typically stitch together several products, each with its own interface, its own tenancy model, and its own upgrade path. OpenNebula replaces that stack with one platform offering a Web UI, a CLI, a REST API, and a Terraform provider over the same resources. The README explicitly positions it as an open alternative to VMware, with a dedicated VMware replacement page, so the ecosystem it lives in is enterprise virtualization and private cloud, and the specific thing it replaces is a proprietary virtualization and cloud management suite. The topics list confirms its base technologies: cloud, KVM, LXC, virtualization, orchestration, virtual-machine.

Key capabilities

• Unified cloud management of virtualized, containerized, and AI workloads from one platform.
• Access interfaces covering the Web UI, CLI, REST API, and a Terraform provider for automation.
• Federated architecture for operating distributed infrastructures across multiple sites and regions.
• Built-in high availability to increase resilience and reduce operational downtime.
• Kubernetes integration with lifecycle management for deploying and scaling clusters.
• Secure multi-tenancy using role-based access control, quotas, isolation, and governance.
• AI Factory Platform support for GPU workloads, model training, LLM inference, and AI services.
• Marketplace of ready-to-use virtual appliances and service templates.

Who uses it and how

• Enterprises standardizing on KVM and LXC virtualization that want a self-hosted control plane for private cloud instead of a proprietary suite.
• Operators running multiple sites or regions, who use the federated architecture to manage them as one distributed infrastructure.
• Platform teams that must run Kubernetes clusters and virtual machines side by side, using the integrated lifecycle management rather than two independent stacks.
• AI and research groups that need GPU-accelerated workloads, model training, and LLM inference on infrastructure they own.
• Contributors and translators: development happens through the GitHub wiki contribution guide, and localization runs through Transifex.

Getting started

The fastest way to evaluate OpenNebula is miniONE, available at github.com/OpenNebula/minione, which automatically deploys a complete OpenNebula environment on a single server in a few minutes. For production environments, follow the installation, upgrade, and architecture and design guides published at docs.opennebula.io/stable.

How it compares

OpenNebula is published under the Apache-2.0 licence and is documented by the project as an open alternative to VMware replacement scenarios. Because it is self-hosted, the operator keeps the control plane on their own infrastructure rather than renting management from the vendor, and the licence carries no per-host or per-socket vendor fee. That contrasts with a proprietary virtualization suite, where the licence, the management layer, and the data all sit inside one commercial product.

When to use it — and when not to

Pick OpenNebula if the goal is a self-hosted private, edge, or hybrid cloud under an Apache-2.0 licence and the team can operate the platform and its supporting infrastructure itself. Do not pick it if you want a fully managed service with no operational burden, or if you need a one-line install: the README gives no package names, Docker image, or compose file, and sends production users to external installation and upgrade guides. The tracker also carries 771 open issues against 1,744 stars, and the README itself is fairly thin on operational specifics, so plan for real reading before a production rollout.