The most scalable and
fully automated Kubernetes service
Put your containers on autopilot, eliminating the need to
manage nodes or capacity and reducing cluster costs—with
little to no cluster operations expertise required.
GKE's
Autopilot mode
is a hands-off, fully managed Kubernetes platform that
manages your cluster’s underlying compute infrastructure
(without you needing to configure or monitor)—while still
delivering a complete Kubernetes experience. And with
per-pod billing,
Autopilot ensures you pay only for your running pods, not
system components, operating system overhead, or unallocated
capacity for up to 85% savings from resource and operational
efficiency.
Privately networked
clusters in GKE can be restricted to a private endpoint or a
public endpoint that only certain address ranges can
access. GKE Sandbox for
the Standard mode of operation provides a second layer of
defense between containerized workloads on GKE for
enhanced workload security.
GKE clusters inherently support Kubernetes Network Policy to
restrict traffic with pod-level firewall rules.
Get access to
enterprise-ready containerized solutions with prebuilt
deployment templates, featuring portability, simplified
licensing, and consolidated billing. These are not just
container images, but open source, Google-built, and
commercial applications that increase developer
productivity. Click to deploy on-premises or in third-party
clouds from
Google Cloud Marketplace.
GKE implements full
Kubernetes API, 4-way autoscaling, release channels,
multi-cluster support, and
scales up to 15000 nodes.
Horizontal pod autoscaling can be based on CPU utilization
or custom metrics. Cluster autoscaling works on a
per-node-pool basis and vertical pod autoscaling
continuously analyzes the CPU and memory usage of pods,
automatically adjusting CPU and memory requests.
Migrate to Containers
makes it fast and easy to modernize traditional applications
away from virtual machines and into containers. Our unique
automated approach extracts critical application elements
from the VM so you can easily insert those elements into
containers in GKE or Anthos clusters without the VM layers
(like Guest OS) that become unnecessary with containers.
This product also works with GKE Autopilot.
Backup for GKE is
an easy way for customers running stateful workloads on GKE
to protect, manage, and restore their containerized
applications and data.
Identity and access
management
Control access in the
cluster with your Google accounts and role
permissions.
Hybrid networking
Reserve an IP address
range for your cluster, allowing your cluster IPs to coexist
with private network IPs via Google Cloud VPN.
Security and compliance
GKE is backed by a
Google security team of over 750 experts and is both HIPAA
and PCI DSS compliant.
Integrated logging and
monitoring
Enable Cloud Logging
and Cloud Monitoring with simple checkbox configurations,
making it easy to gain insight into how your application is
running.
Cluster options
Choose clusters
tailored to the availability, version stability, isolation,
and pod traffic requirements of your workloads.
Auto scale
Automatically scale
your application deployment up and down based on resource
utilization (CPU, memory).
Auto upgrade
Automatically keep your
cluster up to date with the latest release version of
Kubernetes.
Auto repair
When auto repair is
enabled, if a node fails a health check, GKE initiates a
repair process for that node.
Resource limits
Kubernetes allows you
to specify how much CPU and memory (RAM) each container
needs, which is used to better organize workloads within
your cluster.
Container isolation
Use
GKE Sandbox for
a second layer of defense between containerized workloads on
GKE for enhanced workload security.
Stateful application
support
GKE isn't just for
12-factor apps. You can attach persistent storage to
containers, and even host complete databases.
Docker image support
GKE supports the common
Docker container format.
OS built for containers
GKE runs on
Container-Optimized OS, a hardened OS built and managed by
Google.
Private container
registry
Integrating with Google
Container Registry makes it easy to store and access your
private Docker images.
Fast consistent builds
Use Cloud Build to
reliably deploy your containers on GKE without needing to
setup authentication.
Workload portability,
on-premises and cloud
GKE runs Certified
Kubernetes, enabling workload portability to other
Kubernetes platforms across clouds and on-premises.
GPU and TPU support
GKE supports GPUs and
TPUs and makes it easy to run ML, GPGPU, HPC, and other
workloads that benefit from specialized hardware
accelerators.
Built-in dashboard
Google Cloud console
offers useful dashboards for your project's clusters and
their resources. You can use these dashboards to view,
inspect, manage, and delete resources in your
clusters.
Spot VMs
Affordable compute
instances suitable for batch jobs and fault-tolerant
workloads.
Spot VMs provide
significant savings of up to 91% while still getting the
same performance and capabilities as
regular VMs.
Persistent disks support
Durable,
high-performance block storage for container instances. Data
is stored redundantly for integrity, flexibility to resize
storage without interruption, and automatic encryption. You
can
create persistent disks in
HDD or SSD formats. You can also take snapshots of your
persistent disk and create new persistent disks from that
snapshot.
Local SSD support
GKE offers
always-encrypted local solid-state drive (SSD) block
storage. Local SSDs are physically attached to the server
that hosts the virtual machine instance for very high
input/output operations per second (IOPS) and very low
latency compared to persistent disks.
Global load balancing
Global load-balancing
technology helps you distribute incoming requests across
pools of instances across multiple regions, so you can
achieve maximum performance, throughput, and availability at
low cost.
Linux and Windows support
Fully supported for
both Linux and Windows workloads, GKE can run both Windows
Server and Linux nodes.
Hybrid and multi-cloud
support
Take advantage of
Kubernetes and cloud technology in your own data center. Get
the GKE experience with quick, managed, and simple installs
as well as upgrades validated by Google
through Anthos.
Serverless containers
Run stateless
serverless containers abstracting away all infrastructure
management and automatically scale them
with Cloud Run.
Usage metering
Fine-grained visibility
to your Kubernetes clusters. See your GKE clusters' resource
usage broken down by namespaces and labels, and attribute it
to meaningful entities.
Release channels
Release channels
provide more control over which automatic updates a given
cluster receives, based on the stability requirements of the
cluster and its workloads. You can choose rapid, regular, or
stable. Each has a different release cadence and targets
different types of workloads.
Software supply chain
security
Verify, enforce, and
improve security of infrastructure components and packages
used for container images with Container Analysis.
Per-second billing
Google bills in
second-level increments. You pay only for the compute time
that you use.
How It Works
A GKE cluster has a control plane and machines called
nodes. Nodes run the services supporting the
containers that make up your workload. The control
plane decides what runs on those nodes, including
scheduling and scaling. Autopilot mode manages this
complexity; you simply deploy and run your apps!
This hands-on lab shows you how
to create a continuous delivery pipeline using
Google Kubernetes Engine, Google Cloud Source
Repositories, Google Cloud Container Builder, and
Spinnaker. After you create a sample application,
you configure these services to automatically
build, test, and deploy it.
Use Migrate to Containers to
move and convert workloads directly into
containers in GKE. Migrate a two-tiered LAMP stack
application, with both app and database VMs, from
VMware to GKE.
Work with a trusted partner to get
Google Kubernetes Engine on-prem and bring Kubernetes
world-class management to private infrastructure. Or
tap into migration services from the Google Cloud
marketplace.
Create a containerized web app,
test it locally, and then deploy to a Google
Kubernetes Engine (GKE) cluster—all directly in the
Cloud Shell Editor. By the end of this short tutorial,
you'll understand how to build, edit, and debug a
Kubernetes app.
Current, a leading challenger
bank based in New York City, now hosts most of its
apps in Docker containers, including its
business-critical GraphQL API, using GKE to
automate cluster deployment and management of
containerized apps.
Release notes
Documentation
Architecture Center
Training
