Google Kubernetes Engine Reviews and Ratings

Rating: 7.4 out of 10

Score

7.4 out of 10

Community insights

TrustRadius Insights for Google Kubernetes Engine are summaries of user sentiment data from TrustRadius reviews and, when necessary, third party data sources.

Pros

Fast Deployment: Users have appreciated the fast deployment of new GKE clusters in comparison to other cloud providers, highlighting GCP's efficiency in this aspect. The quick deployment process has been a significant factor for users looking to set up and utilize resources promptly.

Up-to-date Kubernetes Version: Reviewers have consistently noted that GCP provides the most up-to-date Kubernetes version, positioning them ahead of competitors in terms of staying current with technology trends. This aspect has garnered positive feedback from users who value working with cutting-edge tools.

Effective Automation for Upgrades: Users found the automation for master upgrades and worker nodes pool in GKE highly effective, streamlining processes for administrators and developers. The automated upgrade system has significantly improved efficiency by reducing manual intervention and ensuring smoother operations.

Reviews

8 Reviews

GKE: Powering Kubernetes Workloads at Scale.

Rating: 8 out of 10

Incentivized

January 6, 2025

Use Cases and Deployment Scope

We have an application for creating Internal reports from Billing Data for customers. We have deployed that application to Google Kubernetes Engine through Gitlab. Also, we provide an automatic secured Kubernetes offering to our customers in Germany.

Pros

Autoscaling Application to mitigate the increase in traffic.
Automatically roll out upgrades to applications.
As it is available through all major Cloud providers, your applications can run anywhere.
Configuration management. Automate the deployment of the application.

Cons

Sometimes, it's not the logical offering if your application is not complex. Managed offerings like App Engine are better in some cases. So, it could have a lightweight offering with free tiers.
For Machine Learning, there are free resources like labs to test. Doing that on Kubernetes is a very tedious task.
It has already improved to be a managed service. But still, developers cannot deploy on Kubernetes without hassle.

Likelihood to Recommend

If your application is complex, if it's planet-scale, or if you need autoscaling, then Kubernetes is best suited. If your application is straightforward, you can opt for App Engine or Cloud Run. In many cases, you can prefer to run the cloud on GKE. But once you deploy on Kubernetes, you get the flexibility to try different things. But if you don't seek flexibility, it's not an option for you.

Verified User

Engineer in Information Technology (10,001+ employees)

Vetted Review

Reseller

2 years of experience

Best-in-market managed Kubernetes solution

Rating: 10 out of 10

Incentivized

June 6, 2022

Use Cases and Deployment Scope

We use GKE to deploy our in-hours custom-made applications along with popular public applications like prometheus/grafana. The applications that we deploy and need to support are both stateful and stateless.

Pros

Deployment of a new GKE cluster is really fast in comparison to other cloud providers.
GCP is ahead other vendors and always provide the most up to date Kubernetes version.
GKE automation for master upgrade and the worker nodes pool works really well.

Cons

Support of IPv6.
Better GitOps.
A "serverless" Kubernetes so we can install Google config connector will be really awesome.
Container-native load balancers do not support internal TCP/UDP load balancers or network load balancers.

Likelihood to Recommend

GKE is the best managed Kubernetes solution out there, it is very well suited to deploy all kinds of application loads for Dev or Production. If you need to migrate your current workload from a monolithic infrastructure or VMs towards a container solution GKE is the go-to for the best results in terms of stability and feature-rich. If you are looking to take full advantage of IPv6 then GKE might not meet your expectations. Container-native load balancers do not support internal TCP/UDP load balancers or network load balancers, functionality of container-native load balancing is currently limited to the HTTP(S) /L7 load balancers only.

Nic Georgakopoulos

DevOps in Engineering at Deloitte Digital (1001-5000 employees)

Vetted Review

3 years of experience

GKE - good if you are using other GCP services

Rating: 8 out of 10

Incentivized

April 28, 2022

Use Cases and Deployment Scope

The major issue that Google Kubernetes Engine solves for us is that as a managed service, it makes maintenance of the cluster much easier - autoscaling and updating nodes, reliable, and cost-effective.

Using the cluster to keep the microservices for an application up and running - it is intended for workloads that can scale.

Pros

easy setup for users
strong documentation
integration with other GCP services

Cons

errors from liveness probes can be ambiguous to resolve

Likelihood to Recommend

Google Kubernetes Engine is well suited for dynamic and large workloads since it can scale up with usage. It is easily configurable, which allows for flexibility.

User interface is simple to navigate, which reduces roadblocks for a team with people unfamiliar with Kubernetes.

Great if you are already using other GCP services as it integrates well with that.

Verified User

Contributor (501-1000 employees)

Vetted Review

2 years of experience

Google Kubernetes Engine: Kubernetes clusters offer an easy-to-start experience and good integration with other Google Cloud services

Rating: 8 out of 10

Incentivized

January 22, 2022

Use Cases and Deployment Scope

Two products I work on are using Google Kubernetes Engine clusters. For the most part, the development efforts mostly go as far as "put service in container," so stuff such as scalability is left to 3rd party components that we use. The Google Kubernetes Engine can use a specific Google-provided ingress controller that is very beneficial when it comes to integrating with other services/products such as Cloud Armor, but it's also vendor-specific, so it has its own quirks and learning curve. Thus, we use the Google Kubernetes Engine just like a regular managed Kubernetes cloud service. The products we have in the Google Kubernetes Engine cluster deal with data piping, collection, and even some machine learning. The major problem that the Google Kubernetes Engine solves for us is a completely managed cloud Kubernetes service - we have an easier time managing our clusters (updates, scaling, and uptime SLA), doing physical and virtual migrations (moving nodes geographically, data in volumes, etc.).

Pros

Engine upgrade rollout strategy - well documented and configurable
Integration with other Google Cloud services like the Compute Engine, SaaS databases, and some cloud networking like Cloud Armor
Graphical interface for a lot of operations - either for a quick peek/overview or actual work done by administrators and/or developers (via the Google Cloud Console, for example)

Cons

It cannot reach true zero scale - they have a competing(?) product for that - Cloud Run Kubernetes clusters. It seems like the Google Kubernetes Engine may not be as flexible as some people need - in terms of costs and infrastructure.
Some networking for the Google Kubernetes Engine is way too "hidden" from other similar services from Google Cloud - like network whitelisting (for the control plane), external IPs(s) are not a part of the VPC network overview, data storage.
We had to make a hack for node-specific changes (max open file descriptors) because we put Elastic in our Google Kubernetes Engine clusters. These changes were made as hacks because there is still no official API/command approach to have such a form of control over the cluster's infrastructure.

Likelihood to Recommend

The Google Kubernetes Engine clusters are very good at being a managed cloud K8s platform - lots of documentation, features, and updates are available. It's also newbie-friendly - for both administrators and developers. Unfortunately, currently, it cannot reach true zero scale - thus, costs (rent for the service) are still involved even if you are barely using it.

Thankfully, it's possible to have alternatives in Google Cloud:

<ul><li>Your own K8s cluster on Compute Engine VMs - you manage it completely; it will have access to a lot of Google Cloud services.</li><li>Cloud Run cluster - less documented but more flexible</li><li>Anthos clusters - you can use this service for a lot of types of K8s clusters - Google Kubernetes Engine, Cloud Run, on-prem, AWS, Azure</li></ul>

Borislav Traykov

DevOps Team Leader in Engineering at Cognyte (1001-5000 employees)

Vetted Review

2 years of experience

View profile

My experience as a first employee at a startup company using GKE

Rating: 9 out of 10

Incentivized

January 6, 2022

Use Cases and Deployment Scope

We are a B2C PrivacyTech company running multiple GKE clusters in different regions. I am the only DevOps engineer at the company responsible for all GCP-related.

Pros

Uptime
Reliability
Easy UI

Cons

Logging
Cost visibility
Dull UI

Likelihood to Recommend

GKE compared to Azure is a lot easier to quickly bootstrap a project for demo purposes. GKE has much better integration with the Kubernetes open-source project and GKE is the first provider to adopt the newest features and it looks like many of the features on the Kubernetes are well suited for GCP purposes.

Verified User

Engineer in Research & Development (11-50 employees)

Vetted Review

3 years of experience

Good decision with GKE

Rating: 9 out of 10

Incentivized

May 15, 2020

Use Cases and Deployment Scope

GKE provides a seamless installation method across a whole organization. It is a fair starting point with Kubernetes technologies. Managed Kubernetes allows deploying application test pipelines for software companies with a reasonable overall price. Moreover, the number of POP helps setup quite reliable installation in a regional way.

Pros

Deployment method (single, zonal, regional).
Lifecycle management (stable, regular, rapid).
Integrated GCE services (loadbalancers).

Cons

Multi-regional deployment (better reliability).
GPU node availability.
Integrated market place.

Likelihood to Recommend

At the moment, the best-managed cluster on the market. Quick deployment with quite specific project requirements. The mesh ingress (istio) allowed the building of a quite complicated upgrade process for applications.

Lukasz Stadnik

Cloud Architect in Engineering at Cloudical (501-1000 employees)

Vetted Review

2 years of experience

Goggle Kubernetes delivered from who knows Google Kubernetes

Rating: 9 out of 10

Incentivized

February 21, 2020

Use Cases and Deployment Scope

Google has made the setup of Google Kubernetes Engine easy in the Google Cloud, it comes somewhere in the middle of Iaas and Paas, the user interface is very intuitive, and you can manage your cluster directly through the web interface. It is an excellent tool with lesser management overhead with servers or Kubernetes installation, etc. At the same time gives you the flexibility to manage the cluster and related settings. You can scale up with a few clicks or set up automated scaling based on traffic and various parameters.

Pros

Automated orchestration, deployment, and scaling of containers
Integrated Logging

Cons

Persistent storage configuration and options.
Routes and external DNS integration

Likelihood to Recommend

Google Kubernetes Engine increases productivity and helps teams focus on their core product without worrying about where to run it. <span style="font-size: 16px;">If your application is very small and simple, a serverless option could be better.</span>

Kadu Barral

Tech Lead in Information Technology at SulAmérica (5001-10,000 employees)

Vetted Review

1 year of experience

View profile

Easy for those already on the Google Cloud platform

Rating: 8 out of 10

Incentivized

August 28, 2019

Use Cases and Deployment Scope

Google Kubernetes Engine is being used by our I.T. Department as part of its Cloud Infrastructure. At this time we have decided on Google Kubernetes Engine as our primary tool for Kubernetes and are running test/evaluation workloads with it. The idea is to drive down our cloud infrastructure costs by moving resources off general-purpose cloud environments.

Pros

Simple setup for new users
Easy integration with existing Google Cloud environments
Cost effective

Cons

Not as intuitive as it could be
Documentation could be better, especially for people using other Google Cloud tools
Not the preferred Kubernetes Engine for many apps

Likelihood to Recommend

Google Kubernetes Engine is a natural addition for users/organizations already using the Google Cloud Platform. For us, it was a simple addition and was up and running within minutes. Our challenge has been that the public documentation for applications that use Kubernetes is aimed at Amazon Web Services, so it requires extra research and work to get running on Google Kubernetes.

Verified User

Manager in Information Technology (51-200 employees)

Vetted Review

1 year of experience

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Google Kubernetes Engine: Kubernetes clusters offer an easy-to-start experience and good integration with other Google Cloud services

Rating: 8 out of 10

Incentivized

January 22, 2022

Use Cases and Deployment Scope

Pros

Engine upgrade rollout strategy - well documented and configurable
Integration with other Google Cloud services like the Compute Engine, SaaS databases, and some cloud networking like Cloud Armor
Graphical interface for a lot of operations - either for a quick peek/overview or actual work done by administrators and/or developers (via the Google Cloud Console, for example)

Cons

It cannot reach true zero scale - they have a competing(?) product for that - Cloud Run Kubernetes clusters. It seems like the Google Kubernetes Engine may not be as flexible as some people need - in terms of costs and infrastructure.
Some networking for the Google Kubernetes Engine is way too "hidden" from other similar services from Google Cloud - like network whitelisting (for the control plane), external IPs(s) are not a part of the VPC network overview, data storage.
We had to make a hack for node-specific changes (max open file descriptors) because we put Elastic in our Google Kubernetes Engine clusters. These changes were made as hacks because there is still no official API/command approach to have such a form of control over the cluster's infrastructure.

Likelihood to Recommend

Thankfully, it's possible to have alternatives in Google Cloud:

Borislav Traykov

DevOps Team Leader in Engineering at Cognyte (1001-5000 employees)

Vetted Review

2 years of experience

View profile