Google Compute Engine is the base and the fundamental part of the infrastructure as a product of the Google Cloud platform. Everything is based on the Compute Engine.

We use Google Compute Engine to create instances and then use the instance to connect and integrate with other products on the platform. I'm a data scientist and I use Vertex AI a lot. Each Vertex AI has a fragment called Compute Engine. With Compute Engine, you can integrate the database and use Vertex AI with it.

I work for an IT company as an architect. I design all the infrastructure and systems, and Google Compute Engine is one of the things that we provide solutions for. Customers using the Google Compute Engine use it for lift and shift models when moving from their on-prem environment to the Google Cloud. Usually, they are running the same type of workload they run on their on-premises on the Google Cloud.

Compute Engine is an option to create virtual machines in GCP. We can set up our service now. We can use Compute Engine for load balancing, and we can categorize it into three different types: general purpose, compute optimized, and memory-optimized. There are a lot of CPU options to choose from, so we can select the one that best suits our needs. We can create Compute Engine instances anywhere in the world, and we can deploy our services to them. This ensures that our application has a global presence.

Another feature of Compute Engine is its networking capability. It provides networking capability to VPCs, which are private cloud networks. Compute Engine also provides instance templates. We can define our virtual machine configuration in an instance template and then use that template to create new virtual machines. We don't need to specify the region or other details when we create an instance template because instance templates are global services. We can use an instance template to create a new virtual machine at any time with the same configuration.

Another service provided by Compute Engine is managed instances. It allows us to create a group of identical virtual machines. The virtual machines in a managed instance group all have the same configuration. We can ensure that a minimum number of instances are always up and running, and if an instance fails, Compute Engine will automatically create a new instance with the same configuration. This ensures that our service is never unavailable.

Compute Engine also provides a load balancer to distribute traffic to our virtual machines. The load balancer provides a single IP address for our service, and it distributes traffic to the virtual machines in our managed instance group. This allows us to scale our service up or down without having to change any client code.

Google Compute Engine is a typical VM. We use it in Kubernetes host machines. 

One of the use cases is autoscaling. It has been quite robust and easy to use for autoscaling. Second, within the console itself, the whole developer experience is far superior as compared to other cloud providers. 

The third, mainly from a networking perspective, is the ability to use multiple regions for load balancing and networking capabilities. I can switch between multiple regions as and when required. 

For specific use cases, in certain types of machines, we don't always get the required compute power. But, it depends from region to region, and sometimes there are resource crunches, and you may not get the required configuration as you need.

We use the solution to host a self-hosted helpdesk.

We use GCE for deploying our applications, websites, web applications, and web services. It deploys our code into production. We use that instance for automated load handling, enabling horizontal scaling and load increases. About 30 to 40 people at my company use it. 

We are using Google Compute Engine for deploying virtual machines for customers, as well as one or two internal uses. We use it mainly, as a managed service we offer customers.

We use Google Compute Engine as a VM. 

We use it primarily for our testing. For example, if you want to deploy a laptop or something to test before deploying something on our appliance site. And we use GCP.

I'm just using it for testing and lab deployment setups.

This is a web application and deployment is moving to GCP for disaster recovery purposes .

I used GCE as simple virtual machines to run applications such as sonar and logstash as I also used to create robust clusters to scale applications with kubernetes.