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We’ve launched a remote MCP server for GKE. Give your agents access to provision, inspect, and resize resources without managing local installation.

Google Kubernetes Engine (GKE)

Kubernetes, evolved: The foundation for platform builders

Put your containers on autopilot and securely run your enterprise workloads at scale—with little to no Kubernetes operational expertise required.

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Get one free zonal or Autopilot cluster per month. Plus, new customers get $300 in free credits to try out GKE.

Features

Simplified cluster management and improved resource efficiency

GKE Autopilot is a mode of operation in which Google manages your node infrastructure, scaling, security, and pre-configured features. With automatic capacity right-sizing and per-pod pricing, you can avoid overprovisioning, overpaying, and underutilization. With the Autopilot’s container-optimized compute platform, you can get near real-time, vertically and horizontally scalable compute that provides the capacity needed, when needed, at the best price and performance.

How Ubie lowers infrastructure costs and maintenance requirements with Autopilot

Learn how

Production-ready platform for agentic AI workloads and gen AI models

With support for up to 65,000-node clusters, integration with AI Hypercomputer, and GPU and TPU support, GKE makes it easy to run ML, HPC, and other workloads that benefit from specialized hardware accelerators.

GKE inference capabilities with gen AI-aware scaling and load balancing techniques help to reduce serving costs by over 30%, tail latency by 60%, and increase throughput by up to 40% compared to other managed and open source Kubernetes offerings.

Secure by design

GKE provides security at scale with built-in best practices, compliant infrastructure, and real-time alerts, so you can quickly and swiftly mitigate security threats and compliance issues in a unified view.

Backed by a Google security team of over 750 experts, GKE’s built-in security posture includes patching and hardening, isolation and segmentation, Confidential GKE Nodes, identity and access management, and integrations with Cloud Logging and Cloud Monitoring. 

Plus, with GKE Sandbox, you can add a second layer of defense between containerized workloads on GKE for enhanced workload security.

Multi-team and multi-cluster management

Fleets and Teams can be used to organize clusters and workloads and assign resources to multiple teams easily to improve velocity and delegate ownership. Team scopes let you define subsets of fleet resources on a per-team basis, with each scope associated with one or more fleet member clusters.

You might choose multiple clusters to separate services across environments, tiers, locales, teams, or infrastructure providers. Fleets strive to make managing multiple clusters as easy as possible.

Workload portability with multi-cloud support

GKE runs Certified Kubernetes and embraces open standards to let customers run their applications, unmodified, on existing on-premises hardware investments or in the public cloud. 

GKE attached clusters lets you register, or attach, any conformant Kubernetes cluster you’ve created yourself to the GKE management environment. Attaching a cluster gives you GKE management and control over it, along with access to additional features like Config Sync, Cloud Service Mesh, and Fleets.

View all features

How It Works

Within each GKE cluster, GKE manages the Kubernetes control plane life cycle from cluster creation to deletion. With Autopilot enabled, GKE can also manage your nodes, including automated provisioning, scaling, and scheduling. Or, you can opt for more control and manage the nodes yourself.

Read the docs

Google Kubernetes Engine in a minute (1:21)

Common Uses

Build platforms for all of your workloads

Build an enterprise developer platform for fast, reliable app delivery

Google Cloud offers a comprehensive suite of managed services and runtimes that act as building blocks for your platform, so you can find the right combination of services for your use cases. GKE’s deep integration with the Google Cloud ecosystem, unmatched scalability, and built-in security posture make it an ideal foundation for your platform.

Build an enterprise platform on GKE

• Watch how ANZ built their IDP with GKE

• Your guide to Golden Paths

• Learn how to improve DevEx with platform engineering

Tutorials, quickstarts, & labs

Build an enterprise developer platform for fast, reliable app delivery

Google Cloud offers a comprehensive suite of managed services and runtimes that act as building blocks for your platform, so you can find the right combination of services for your use cases. GKE’s deep integration with the Google Cloud ecosystem, unmatched scalability, and built-in security posture make it an ideal foundation for your platform.

Build an enterprise platform on GKE

• Watch how ANZ built their IDP with GKE

• Your guide to Golden Paths

• Learn how to improve DevEx with platform engineering

Learning resources

What is platform engineering?

Platform engineering is the practice of designing and maintaining an internal developer platform to equip software engineering teams with Golden Paths.

Read to learn more

• Learn how Google does it: Your guide to platform engineering

• Read the latest research on platform engineering success

• 5 myths about platform engineering

Train, serve, and scale gen AI models

Deploy gen AI inference with GKE

GKE not only provides a platform for AI, but it also simplifies and automates Kubernetes operations with AI. With support for up to 65,000 nodes and integration with AI Hypercomputer, you can train and scale your largest gen AI models on GKE. 

Plus, GKE’s Gen AI-aware inference capabilities provide up to 30% lower serving costs, 60% lower tail latency, and 40% higher throughput than OSS K8s.

Learn more about AI/ML model inference on GKE

• Run best practice inference on GKE with Quickstart recipes

• Optimize costs for LLM-service workloads on GKE

• GKE Inference capabilities to reduce costs, tail latency, and increase throughput

Tutorials, quickstarts, & labs

Deploy gen AI inference with GKE

GKE not only provides a platform for AI, but it also simplifies and automates Kubernetes operations with AI. With support for up to 65,000 nodes and integration with AI Hypercomputer, you can train and scale your largest gen AI models on GKE. 

Plus, GKE’s Gen AI-aware inference capabilities provide up to 30% lower serving costs, 60% lower tail latency, and 40% higher throughput than OSS K8s.

Learn more about AI/ML model inference on GKE

• Run best practice inference on GKE with Quickstart recipes

• Optimize costs for LLM-service workloads on GKE

• GKE Inference capabilities to reduce costs, tail latency, and increase throughput

Multi-agent orchestration

Deploy and orchestrate multi-agent applications 

Agentic AI is centered around the orchestration and execution of agents that use LLMs as a "brain" to perform actions through tools. 

GKE is the definitive open platform to support agents and orchestrate your compute so you can embrace the next generation of agentic AI workloads.

Deploy an agentic AI app on GKE

• Learn more about AI/ML orchestration on GKE

Tutorials, quickstarts, & labs

Deploy and orchestrate multi-agent applications 

Agentic AI is centered around the orchestration and execution of agents that use LLMs as a "brain" to perform actions through tools. 

GKE is the definitive open platform to support agents and orchestrate your compute so you can embrace the next generation of agentic AI workloads.

Deploy an agentic AI app on GKE

• Learn more about AI/ML orchestration on GKE

Pricing

How GKE pricing works	After free credits are used, the total cost is based on cluster operation mode, cluster management fees, and applicable inbound data transfer fees.
Service	Description	Price (USD)
Free tier	The GKE free tier provides $74.40 in monthly credits per billing account that are applied to zonal and Autopilot clusters.	Free
Cluster management fee	Includes fully automated cluster life cycle management, pod and cluster autoscaling, cost visibility, automated infrastructure cost optimization, and multi-cluster management features, at no extra cost.	$0.10 per cluster per hour
Compute	When using Autopilot, only pay for the CPU, memory, and compute resources that are provisioned for your pods. For node pools and compute classes that don't use Autopilot, you're billed for the nodes' underlying Compute Engine instances until the nodes are deleted.	Refer to Compute Engine pricing

Learn more about GKE pricing. View all pricing details.

How GKE pricing works

After free credits are used, the total cost is based on cluster operation mode, cluster management fees, and applicable inbound data transfer fees.

Free tier

Description

The GKE free tier provides $74.40 in monthly credits per billing account that are applied to zonal and Autopilot clusters.

Price (USD)

Free

Cluster management fee

Description

Includes fully automated cluster life cycle management, pod and cluster autoscaling, cost visibility, automated infrastructure cost optimization, and multi-cluster management features, at no extra cost.

Price (USD)

$0.10

per cluster per hour

Compute

Description

When using Autopilot, only pay for the CPU, memory, and compute resources that are provisioned for your pods.

For node pools and compute classes that don't use Autopilot, you're billed for the nodes' underlying Compute Engine instances until the nodes are deleted. 

Price (USD)

Refer to Compute Engine pricing

Learn more about GKE pricing. View all pricing details.

Pricing calculator

Estimate your monthly GKE costs, including region specific pricing and fees.

Estimate your costs

Custom quote

Connect with our sales team to get a custom quote for your organization.

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Start your proof of concept

Get started with one free cluster per month.

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Want to learn more about GKE?

Read the docs

Deploy an app to a GKE cluster

Try the quickstart

Find solutions with simple click to deploy to GKE

Find Kubernetes apps

Get expert help evaluating and implementing GKE

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Business Case

Learn from GKE customers

---

10 years and counting: Why Signify chose GKE

With GKE as its foundation, the Philips Hue Platform has scaled its infrastructure to support a 1,150% increase in transactions and commands over the past decade.

Read Signify's story

Unlock AI innovation on GKE

AI-powered advertising provider Moloco gets 10x faster model training times with TPUs on GKE.

Read the blog

With TPUs on GKE, HubX reduces latency by up to 66%, leading to a better user experience and increased conversion rates.

Watch the video

LiveX AI achieves over 50% lower TCO, 25% faster time-to-market, and 66% lower operational cost with GKE Autopilot.

Read the blog

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