Google Kubernetes Engine Professional, Scientific, and Technical Services Reviews & Insights

Score8.1 out of 10

68 Reviews and Ratings

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.

Google Kubernetes Engine Reviews

3 Reviews

Professional, Scientific, and Technical ServicesInformation Technology & Services2Computer & Network Security1

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 (Information Technology & Services company, 10,001+ employees)

Vetted Review

Reseller

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:

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

Borislav Traykov

DevOps Team Leader in Engineering at Cognyte (Information Technology & Services, 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 (Computer & Network Security company, 11-50 employees)

Vetted Review

3 years 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

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

Borislav Traykov

DevOps Team Leader in Engineering at Cognyte (Information Technology & Services, 1001-5000 employees)

Vetted Review

2 years of experience

View profile