Press and Media Resources

New platform combines virtual clusters, dynamic GPU autoscaling, and hybrid networking to maximize NVIDIA infrastructure efficiency—from DGX systems to bare metal and cloud.

vCluster Labs announced its Infrastructure Tenancy Platform for AI at KubeCon North America 2025, delivering a Kubernetes-native foundation for running AI workloads on NVIDIA GPU infrastructure. The platform introduces vCluster Private Nodes, Auto Nodes with Karpenter-based autoscaling, vCluster VPN for hybrid networking, and direct integrations with NVIDIA Base Command Manager, KubeVirt, and Netris—helping organizations maximize GPU utilization while maintaining full workload isolation and security across cloud, on-prem, and bare metal environments.

vCluster Labs and Netris team up to bring cloud-grade Kubernetes automation and network-level multi-tenancy to AI factories and GPU-powered infrastructure.

vCluster Labs has partnered with Netris to revolutionize how AI operators run Kubernetes on GPU infrastructure. By combining vCluster’s Kubernetes-level isolation with Netris’s network automation, the integration delivers a full-stack multi-tenancy solution, simplifying GPU cloud operations, maximizing utilization, and enabling cloud-grade performance anywhere AI runs.

Dynamic, isolated, and cloud-agnostic autoscaling for every virtual cluster.

vCluster Auto Nodes brings dynamic, Karpenter-powered autoscaling to any environment, public cloud, private cloud, or bare metal. Combined with Private Nodes, it delivers true isolation and elasticity for Kubernetes, letting every virtual cluster scale independently without cloud-specific limits.

Back it up and put it to sleep

I’m excited to present the updates coming with vCluster v0.24. In this post, we will cover Snapshots and Sleep Mode Improvements. Along with this post, we have a couple of videos that will demo both features. Snapshots Let’s start by talking about Snapshots. While you were always...

Today, we broke the news about raising our latest round of funding, and in this blog post, I want to share a bit of additional context regarding why we are building this company, the journey of how we got here, and what the road ahead looks like. While we maintain quite a few ope...

Loft Labs’ “Building a Kubernetes Platform” series kicks off on February 29, 2024

vCluster.Pro delivers added security and optimization features to unleash the full potential of virtual Kubernetes clusters in the enterprise.

DevPod is the first and only tool for creating and managing dev environments that does not require a heavyweight server-side setup.

Introducing Loft v3: Enhanced multi-tenancy, GitOps integration, and a revamped UI for streamlined Kubernetes platform management.

Introducing Loft v3

The CNCF Sandbox will provide a neutral home for the project to receive external contributions from the cloud-native community and to benefit from vendor-independent governance.

Streamline your Kubernetes deployments by integrating Loft's virtual clusters with Argo CD for seamless GitOps workflows.

Now, virtual clusters created in Loft are automatically recognized in Argo CD including user permissions and access control settings, so that Argo CD users can deploy to the virtual cluster without any additional effort.

Streamline virtual Kubernetes cluster deployment with Loft Labs' new open-source Cluster API provider for vCluster.

Now, deploying virtual clusters can be done exactly the same as deploying physical Kubernetes clusters on a cloud platform or on-premises data center.

Introducing a plugin system and SDK to customize and extend vcluster's synchronization logic for enhanced Kubernetes multi-tenancy.

vcluster users can now define which resources get synchronized to the underlying Kubernetes cluster and how.

vcluster now supports upstream Kubernetes, enhancing compatibility and flexibility for virtual cluster deployments.

Loft Labs announces that vcluster, the open-source virtual cluster solution, now supports upstream Kubernetes. This update allows users to deploy virtual clusters using standard Kubernetes distributions, improving resource utilization and enabling immediate access to new Kubernetes features.

Loft Labs secures $4.6 million in seed funding to enhance self-service Kubernetes access for engineering teams.

Loft Labs announces a $4.6 million seed funding round led by Fusion Fund, aiming to scale self-service Kubernetes access for engineers through its platform, Loft.

Abby Kearns, CTO at Puppet and former CEO of the Cloud Foundry Foundation, brings her cloud-native expertise to Loft Labs’ advisory board.

Loft Labs welcomes Abby Kearns, a seasoned technology executive with deep experience in cloud-native and open-source technologies, to its advisory board. As CTO at Puppet and former CEO of the Cloud Foundry Foundation, Kearns offers strategic guidance to support Loft Labs' growth in the Kubernetes ecosystem.

Introducing jsPolicy: A JavaScript-based policy engine simplifying Kubernetes policy management for enhanced clarity and maintainability.

Loft Labs announces jsPolicy, an open-source Kubernetes policy engine utilizing JavaScript to make policy creation and management more accessible and maintainable. By leveraging the familiar syntax of JavaScript and the performance of Google's V8 engine, jsPolicy aims to streamline policy enforcement in Kubernetes clusters.

Loft Labs introduces vCluster, an open-source solution enabling lightweight, virtual Kubernetes clusters for enhanced multi-tenancy and cost efficiency.

Loft Labs has open-sourced vCluster, a pioneering virtual cluster technology for Kubernetes. vCluster allows organizations to deploy lightweight, fast virtual clusters within a single Kubernetes cluster, facilitating workload consolidation, resource sharing, and significant infrastructure cost savings.

With Loft, any Kubernetes cluster becomes a self-service platform where engineers can create namespaces and virtual clusters whenever they need them. At the same time, Loft's sleep mode and cluster sharing technologies help eliminate idle workloads and save cloud computing costs.