Google Kubernetes Engine documentation
Deploy, manage, and scale containerized applications on Kubernetes, powered by Google Cloud.
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Architecting with Kubernetes Engine
This course features a combination of lectures, demos, and hands-on labs to help you explore and deploy solution elements—including infrastructure components like pods, containers, deployments, and services—along with networks and application services.
Training
Training and tutorials
Kubernetes Qwik Start lab
Learn how to deploy a containerized application with Kubernetes Engine in less than 30 minutes.
Training
Training and tutorials
Serve LLMs for AI/ML inference using GPUs on GKE with vLLM
This tutorial demonstrates how to use graphical processinng units (GPUs) on GKE to run large language models (LLMs) for AI/ML inference.
Training
Training and tutorials
Create a cluster and deploy a workload in the Google Cloud console
Learn how to create a Kubernetes cluster and deploy a 'hello world' web app in Google Cloud console.
Training
Training and tutorials
Setting up HTTP(S) Load Balancing with Ingress
This tutorial shows how to run a web application behind an external HTTP(S) load balancer by configuring the Ingress resource.
Training
Training and tutorials
Configuring Domain Names with Static IP Addresses
This tutorial demonstrates how to expose your web application to the internet on a static external IP address and configure DNS records of your domain name to point to your application.
Use case
Use cases
Best practices for continuous integration and delivery to Google Kubernetes Engine
Learn best practices for continuous integration and continuous delivery to GKE, from source control to deployment strategies.
CI/CD
GitOps
Use case
Use cases
Configuring privately used public IPs for GKE
Apply privately used public IP addresses for Google Kubernetes Engine pod address blocks.
VPC
Networking
Use case
Use cases
Best practices for running cost-optimized Kubernetes applications on GKE
Take advantage of the elasticity provided by Google Cloud when running cost-optimized applications on GKE.
Costs
Use case
Use cases
Modernization path for .NET applications on Google Cloud
Learn a gradual and structured process for modernizing monolithic applications.
Windows
.NET
Migration
Except as otherwise noted, the content of this page is licensed under the Creative Commons Attribution 4.0 License, and code samples are licensed under the Apache 2.0 License. For details, see the Google Developers Site Policies. Java is a registered trademark of Oracle and/or its affiliates.
Last updated 2025-08-29 UTC.
[[["Easy to understand","easyToUnderstand","thumb-up"],["Solved my problem","solvedMyProblem","thumb-up"],["Other","otherUp","thumb-up"]],[["Hard to understand","hardToUnderstand","thumb-down"],["Incorrect information or sample code","incorrectInformationOrSampleCode","thumb-down"],["Missing the information/samples I need","missingTheInformationSamplesINeed","thumb-down"],["Other","otherDown","thumb-down"]],["Last updated 2025-08-29 UTC."],[],[],null,["# Google Kubernetes Engine documentation\n======================================\n\n[Read product documentation](/kubernetes-engine/docs/concepts/kubernetes-engine-overview)\nDeploy, manage, and scale containerized applications on Kubernetes, powered by Google Cloud.\n[Learn more](/kubernetes-engine/docs/concepts/kubernetes-engine-overview)\n[Get started for free](https://console.cloud.google.com/freetrial) \n\n#### Start your proof of concept with $300 in free credit\n\n- Get access to Gemini 2.0 Flash Thinking\n- Free monthly usage of popular products, including AI APIs and BigQuery\n- No automatic charges, no commitment \n[View free product offers](/free/docs/free-cloud-features#free-tier) \n\n#### Keep exploring with 20+ always-free products\n\n\nAccess 20+ free products for common use cases, including AI APIs, VMs, data warehouses,\nand more.\n\nDocumentation resources\n-----------------------\n\nFind quickstarts and guides, review key references, and get help with common issues. \nformat_list_numbered\n\n### Guides\n\n-\n\n [Quickstart: Create a cluster and deploy a workload](/kubernetes-engine/docs/quickstarts/create-cluster)\n\n-\n\n [Configuring cluster access for kubectl](/kubernetes-engine/docs/how-to/cluster-access-for-kubectl)\n\n-\n\n [Access Google Cloud APIs from GKE workloads](/kubernetes-engine/docs/how-to/workload-identity)\n\n-\n\n [GKE security documentation](/kubernetes-engine/docs/concepts/security-overview)\n\n-\n\n [GKE networking documentation](/kubernetes-engine/docs/concepts/network-overview)\n\ninfo\n\n### AI/ML on GKE tutorials\n\n-\n\n [AI/ML orchestration on GKE](/kubernetes-engine/docs/integrations/ai-infra)\n\n-\n\n [Core concept: About GPUs in GKE](/kubernetes-engine/docs/concepts/gpus)\n\n-\n\n [Core skill: Use GPUs in GKE](/kubernetes-engine/docs/how-to/gpus)\n\n-\n\n [Serve Llama open models using GPUs with vLLM](/kubernetes-engine/docs/tutorials/serve-llama-gpus-vllm)\n\n-\n\n [Serve Gemma open models using GPUs with vLLM](/kubernetes-engine/docs/tutorials/serve-gemma-gpu-vllm)\n\n-\n\n [Serve Gemma open models with Hugging Face TGI](/kubernetes-engine/docs/tutorials/serve-gemma-gpu-tgi)\n\n-\n\n [Serve an LLM with multiple GPUs in GKE](/kubernetes-engine/docs/tutorials/serve-multiple-gpu)\n\n-\n\n [Deploy GPUs for batch workloads with Dynamic Workload Scheduler](/kubernetes-engine/docs/how-to/provisioningrequest)\n\n-\n\n [About Ray on GKE](/kubernetes-engine/docs/add-on/ray-on-gke/concepts/overview)\n\ngroup_work\n\n### References and resources\n\n-\n\n [REST API](/kubernetes-engine/docs/reference/rest)\n\n-\n\n [API permissions](/kubernetes-engine/docs/reference/api-permissions)\n\n-\n\n [API organization and structure](/kubernetes-engine/docs/reference/api-organization)\n\n-\n\n [gcloud container commands](/sdk/gcloud/reference/container)\n\n-\n\n [Kubernetes documentation](http://kubernetes.io/docs/)\n\n-\n\n [kubectl commands](https://kubernetes.io/docs/reference/generated/kubectl/kubectl-commands)\n\n-\n\n [Release notes](/kubernetes-engine/docs/release-notes)\n\n-\n\n [Release schedule](/kubernetes-engine/docs/release-schedule)\n\n-\n\n [Security bulletins](/anthos/clusters/docs/security-bulletins)\n\n-\n\n [Security patching](/kubernetes-engine/docs/resources/security-patching)\n\n-\n\n [Pricing](/kubernetes-engine/pricing)\n\nRelated resources\n-----------------\n\nTraining and tutorials \nUse cases \nExplore self-paced training, use cases, reference architectures, and code samples with examples of how to use and connect Google Cloud services. Training \nTraining and tutorials\n\n### Architecting with Kubernetes Engine\n\n\nThis course features a combination of lectures, demos, and hands-on labs to help you explore and deploy solution elements---including infrastructure components like pods, containers, deployments, and services---along with networks and application services.\n\n\n[Learn more](http://cloud.google.com/training/course/architecting-with-google-kubernetes-engine) \nTraining \nTraining and tutorials\n\n### Kubernetes Qwik Start lab\n\n\nLearn how to deploy a containerized application with Kubernetes Engine in less than 30 minutes.\n\n\n[Learn more](https://www.cloudskillsboost.google/catalog_lab/911?qlcampaign=77-18-gcpd-236&utm_source=gcp&utm_medium=documentation&utm_campaign=kubernetes) \nTraining \nTraining and tutorials\n\n### Serve LLMs for AI/ML inference using GPUs on GKE with vLLM\n\n\nThis tutorial demonstrates how to use graphical processinng units (GPUs) on GKE to run large language models (LLMs) for AI/ML inference.\n\n\n[Learn more](/kubernetes-engine/docs/tutorials/serve-gemma-gpu-vllm) \nTraining \nTraining and tutorials\n\n### Create a cluster and deploy a workload in the Google Cloud console\n\n\nLearn how to create a Kubernetes cluster and deploy a 'hello world' web app in Google Cloud console.\n\n\n[Learn more](/kubernetes-engine/docs/quickstarts/create-cluster) \nTraining \nTraining and tutorials\n\n### Setting up HTTP(S) Load Balancing with Ingress\n\n\nThis tutorial shows how to run a web application behind an external HTTP(S) load balancer by configuring the Ingress resource.\n\n\n[Learn more](/kubernetes-engine/docs/tutorials/http-balancer) \nTraining \nTraining and tutorials\n\n### Configuring Domain Names with Static IP Addresses\n\n\nThis tutorial demonstrates how to expose your web application to the internet on a static external IP address and configure DNS records of your domain name to point to your application.\n\n\n[Learn more](/kubernetes-engine/docs/tutorials/configuring-domain-name-static-ip) \nUse case \nUse cases\n\n### Best practices for continuous integration and delivery to Google Kubernetes Engine\n\n\nLearn best practices for continuous integration and continuous delivery to GKE, from source control to deployment strategies.\n\nCI/CD GitOps\n\n\u003cbr /\u003e\n\n[Learn more](/solutions/best-practices-continuous-integration-delivery-kubernetes) \nUse case \nUse cases\n\n### Configuring privately used public IPs for GKE\n\n\nApply privately used public IP addresses for Google Kubernetes Engine pod address blocks.\n\nVPC Networking\n\n\u003cbr /\u003e\n\n[Learn more](/solutions/configuring-privately-used-public-ips-for-GKE) \nUse case \nUse cases\n\n### Best practices for running cost-optimized Kubernetes applications on GKE\n\n\nTake advantage of the elasticity provided by Google Cloud when running cost-optimized applications on GKE.\n\nCosts\n\n\u003cbr /\u003e\n\n[Learn more](/architecture/best-practices-for-running-cost-effective-kubernetes-applications-on-gke) \nUse case \nUse cases\n\n### Modernization path for .NET applications on Google Cloud\n\n\nLearn a gradual and structured process for modernizing monolithic applications.\n\nWindows .NET Migration\n\n\u003cbr /\u003e\n\n[Learn more](/solutions/modernization-path-dotnet-applications-google-cloud)\n\nRelated videos\n--------------\n\n### Try GKE for yourself\n\nCreate an account to evaluate how our products perform in real-world scenarios. \nNew customers also get $300 in free credits to run, test, and deploy workloads. \n[Try GKE free](https://console.cloud.google.com/freetrial)"]]
