srelens vs vCluster Platform

srelens is a Kubernetes desktop workspace for browsing, inspecting, and operating clusters. It covers more than 40 resource kinds, including workloads, networking, storage, RBAC, admission, autoscaling, and custom resources. Investigation tools include live watches, log streaming, in-pod terminals, port forwarding, Helm release views, and a schema-aware YAML editor. CPU and memory metrics for nodes and pods come through Kubernetes Metrics Server. Users can scale or restart workloads and cordon, drain, or evict nodes; destructive actions require confirmation. The desktop app connects directly to Kubernetes API servers using local kubeconfig credentials, without an intermediary srelens cloud service. Available operations depend on the selected context’s credentials and RBAC permissions. Added kubeconfig files stay local, and pasted configs are stored in the application directory. Secret values are masked by default and revealed per key. The built-in MCP server connects to clients such as Claude Code, Claude Desktop, Cursor, and Codex. srelens is free, open source under the MIT license, and requires no account or license key.

vCluster Platform is a management plane for deploying, configuring, and operating tenant cluster fleets through a web UI, CLI, and API. It manages tenants across EKS, GKE, AKS, bare metal, and private cloud, with isolated tenant clusters and shared-node, dedicated-node, private-node, Auto Nodes, and Standalone tenancy models. With vCluster Cloud, the platform is hosted while host and virtual clusters remain in the customer's infrastructure; self-hosted deployment puts the full platform there. The Free plan is free and permits unlimited virtual clusters within a limit of 64 CPUs or 32 GPUs, with a maximum of one HA vCluster. It also supports provisioning, multi-cluster management, policy enforcement, and upgrade management. Listed integrations include Terraform, Rancher, Argo CD, HashiCorp Vault, and Istio. Enterprise features include SSO, audit logging, vulnerability alerts, security review, FIPS-compliant images, enterprise support, and SLAs. One isolation caveat is that shared nodes share several underlying components across tenants, including the kernel, kubelet, node filesystem, and host network. The maker describes use by AI cloud providers and public and private cloud teams.