--- title: "Managed Kubernetes in India — GPU Clusters, Node Pools & Autoscaling" description: "Managed Kubernetes on E2E Cloud: launch a cluster in minutes with autoscaling node pools, Ceph-backed persistent volumes, MetalLB LoadBalancer services and kubectl access. Run GPU workloads with H100/H200 and MIG partitioning. INR billing, Indian DCs." url: "https://www.e2enetworks.com/managed-kubernetes" canonical: "https://www.e2enetworks.com/managed-kubernetes" provider: "E2E Networks Limited" type: "Service" keywords: ["managed kubernetes india", "gpu kubernetes cluster", "kubernetes cloud india", "k8s node pool autoscaling india", "kubernetes gpu mig"] priceCurrency: "INR" taxNote: "Prices exclude GST" region: "India (Delhi NCR, Chennai)" generated: "2026-09-11" --- # Managed Kubernetes in India — GPU Clusters, Node Pools & Autoscaling > Managed Kubernetes on E2E Cloud: launch a cluster in minutes with autoscaling node pools, Ceph-backed persistent volumes, MetalLB LoadBalancer services and kubectl access. Run GPU workloads with H100/H200 and MIG partitioning. INR billing, Indian DCs. Canonical page: https://www.e2enetworks.com/managed-kubernetes Managed Kubernetes, on India's GPU cloud. Launch a cluster in minutes with autoscaling node pools, Ceph-backed persistent volumes and MetalLB LoadBalancer services — then point kubectl at it. Same platform, same INR billing, same Indian data centers as your GPUs. ## At a glance - **Minutes** — Cluster provisioning - **Multi-pool** — Static + autoscale node pools - **kubectl** — Standard kubeconfig access - **99.9%** — Uptime SLA ## Platform capabilities - **Versioned clusters** — Choose your Kubernetes version at creation, with release and support status labelled. Upgrade the master node plan later as control-plane load grows — no downgrade surprises. - **Worker node pools** — Group workloads into pools with their own CPU/memory/disk configs. Add, edit, resize, reboot or delete pools independently — compute-heavy and memory-heavy apps stop fighting. - **Pool autoscaling** — AutoScale pools scale between min and max on CPU or memory thresholds, a custom attribute you push via API, or a cron schedule — the same policy engine as Auto Scaling. - **Persistent volumes** — Console-managed PVs plus dynamic provisioning through a Ceph RBD CSI StorageClass. PVCs bind on demand, volumes expand online (never shrink — the K8s API forbids it, and we say so). - **MetalLB LoadBalancer** — Native MetalLB integration: reserve service IPs into LB IP pools and expose Services of type LoadBalancer on floating external IPs that survive node failure. Or front it with a managed LB. - **GPU & MIG ready** — Run inference and training on E2E GPU nodes. MIG partitioning on H100/H200 slices one GPU into hardware-isolated instances for notebooks and lightweight serving — see TIR for the fully managed route. ## How it works 1. **Configure the cluster** — Pick the Kubernetes version, a default or custom VPC (mandatory — all nodes draw IPs from its pool), optional cluster encryption, and a security group allowing port 6443 for the API server. 2. **Add node pools** — Create static pools with a fixed worker count, or AutoScale pools with min/max nodes and an elastic, scheduled, or combined scaling policy. 3. **Create & connect** — Click Create Cluster; minutes later download the kubeconfig, run kubectl --kubeconfig=... proxy, and sign in to the Kubernetes dashboard with your token. 4. **Ship workloads** — Pull images from the private container registry with registry secrets, claim PVCs, reserve LoadBalancer IPs, and watch monitoring, alerts and the activity timeline. ## Node pool scaling policies | Policy | Scales on | How it is configured | | --- | --- | --- | | Elastic — Default (CPU) | CPU utilization threshold | Set the threshold; nodes are added as CPU rises and removed as it falls | | Elastic — Default (Memory) | Memory utilization threshold | Same mechanics, driven by memory instead of CPU | | Elastic — Custom | Any attribute you define (e.g. NETTX, DISKWRIOPS, queue depth) | Name the parameter, set up/down thresholds, watch period and cooldown; update its value via a generated cURL command from scripts, hooks or cron | | Scheduled | Time — cron recurrence | Upscale and downscale recurrences with target node counts for predictable peaks | | Elastic + Scheduled | Both together | Combine a metric-driven policy with a time-driven baseline | ## Platform details - **Security by default** — Security groups as virtual firewalls on every cluster (at least one always attached), optional cluster encryption at creation, private IPv4 within your VPC, and automatic SSL certificate renewal for the cluster — manual override included. - **Monitoring & alerts** — Free per-node monitoring — CPU, disk read/write, network traffic — plus threshold alerts (trigger type, operator, severity) routed to user groups, and a full activity timeline of cluster actions. - **Registry & secrets** — Kubernetes Secrets keep credentials out of images; docker-registry secrets pull from E2E's private registry (registry.e2enetworks.net) with one kubectl command. ## Frequently asked questions ### Do I get real kubectl access? Yes — download the kubeconfig from the cluster details page and use standard kubectl against the API server (port 6443 must be open in the security group). The Kubernetes dashboard is available via kubectl proxy with token sign-in. ### Can node pools scale to match GPU inference load? Pools scale on CPU, memory, cron schedules, or any custom attribute you publish — including queue depth or request backlog from an inference service. For fully managed autoscaling model endpoints without cluster ops, see the TIR platform, which is Kubernetes-native under the hood. ### What is MIG and when should I use it? Multi-Instance GPU partitions one physical NVIDIA GPU (supported on H100 and H200) into hardware-isolated instances, each appearing as its own GPU type in Kubernetes via the NVIDIA device plugin. It's offered with E2E private clusters and suits notebooks, inference services and lightweight training that don't need a full GPU. ### Can I resize things after launch? Yes — resize node pools, add or delete pools, power nodes on/off and reboot them, and upgrade the master node plan to a higher tier in the same series (downgrades aren't allowed). Cluster deletion removes all nodes, volumes and configuration permanently. ### Where do my clusters physically run? In E2E's Indian data centers (Delhi NCR and Chennai), operated by an NSE & BSE-listed Indian company — relevant if you have data-residency obligations. ## Related pages - [Container Registry for cluster images](https://www.e2enetworks.com/container-registry) - [Block Storage and CSI volumes](https://www.e2enetworks.com/block-storage) - [GPU instances for node pools](https://www.e2enetworks.com/gpus) - [Node pricing](https://www.e2enetworks.com/pricing) ## Get started Bring your own manifests, we handle the rest. Pricing: https://www.e2enetworks.com/pricing · Talk to sales: https://www.e2enetworks.com/contact-sales - [Start Free](https://myaccount.e2enetworks.com/accounts/signup) - [Talk to an Engineer](https://www.e2enetworks.com/contact-sales) --- This Markdown is generated from the same data that renders https://www.e2enetworks.com/managed-kubernetes. Provider: E2E Networks Limited (NSE: E2E), India. Site index for AI agents: https://www.e2enetworks.com/llms.txt