Google Compute Engine Reviews & Insights

Our entire server infrastructure runs on Google Compute Engine. This includes web, authentication and CI/CD servers (Jenkins). We can easily scale by deploying virtual instances as needed with just a few clicks. Tasks such as backup and monitoring are handled automatically, allowing our team to stay lean and focus on our main subject matter.

It's ideal for startups looking to launch a SaaS product to the market relatively fast. Even better if growth estimation are steady to forecast costs accurately. It can become expensive for scenarios where growth can't be well estimated.

Google Compute Engine is a stateful compute service in GCP, so the business problems we try to use it for are deploying different solutions it can be AI solutions, for any other. Recently we are using for the deployment of LLMs, we are using it to deploy Open Source LLMs up there for security closure which has to be shown to clients. Being a server backed compute the main monitoring and flexibility of setup is the main benefit in my views, perfect for learning as well as noting down the last detail of the solution in terms of requirements or maintenance workloads.

The scenarios where setups should be most flexible, monitoring you can setup as per needs, and high-performance requirement is a must, even we can setup the security premises as well. All of the setup is in your hand and as per your requirements and resources you can set up the whole solutioning till the very last inch of it.

Well, Google Compute Engine is basically Google's response to AWS EC2 or Azure VMs. It's a cloud VM service that provides you with VMs on demand, with various types and capabilities for your needs. Of course, it's flexible in terms of how many resources you want and whether you want auto-scaling or not. Also, enterprise features like IAM and VPC are supported.

I would only pick Google Compute Engine if I already used other GCP services. In my company, some projects use GCP, so GCE is the natural choice for those. However, the same can be said for projects on AWS/Azure. When you need a VM, it's usually much simpler to just use the service on the platform that you're already using.

Google Cloud compute engine (GCE) is the backbone of all Google Cloud for us. Anything you plan to do needs computing, and GCE works well with all the offerings of different CPU, RAM, and GPU configurations for various workloads. We use GCE directly for stateful services while it is used under the hood for k8s clusters and other services.

It is excellent if you have any workloads that need raw computing or plan to have any state-full services running in your environment like DBs (for which you don't want to use Managed services), cache, etc. It also gives you complete control over which versions of software, OS, etc., you need, and thus, you can build anything and deploy it on GCE.

We use Google Compute Engine (GCP) for compute (Virtual Machines) and GKE. Our use case is to provide seamless multi-cloud network connectivity between on-premise networks and Google Compute Engine VPC Network. My switching between on-premise and cloud resources, we aim to optimize cloud cost as well to enterprise customers.

Scalability is key aspect of Google Compute Engine (GCP). When in need to launch 100s of machines, Google Compute Engine (GCP) does really well. Google buckets is another excellent service we use to manage storage.

For a very long period, we have been utilising Google Compute Engine. Our application data is hosted on virtual machines that we have been building.We have utilised it to host Vector Databases in addition to our front-end and back-end databases.For even quicker processing, we have been employing the virtual machines offered by the TPU, and the greatest part is that they are always 100% up and running.

It's ideal for you, as I've already shown, if you wish to host front-end, back-end, vector databases, and other website components.It provides 100% uptime due to its security, which is essential for both new and existing businesses.However, Firebase is also an excellent front-end option because it comes from Google.Because GCP provides so many options, it can be a little confusing for beginning users and is quite pricey and technical.

We run a number of services via Google Compute Engine, particularly beta and support services as adjunct to our standard Kubernetes deployments. Google Compute Engine is very useful for prototyping and deployments with specific needs. Google Compute Engine allows us to use consistent deployment practices while still allowing experimentation outside of GKE (for example, via IAC/Terraform/Helm).

Google Compute Engine works well in scenarios where horizontal scaling isn't important, where more detailed control over host parameters is required, and for one-off experimentation. It works less will when horizontal scaling is important (especially when compared to Kubernetes on Google Kubernetes Engine). It also may require more management overhead than something like GKE, depending on your deployment practices.

We are using Google Compute Engine for running our workloads with respect to scaling our platform using templates. The ease of creating the compute engine with compatibility of Operating systems and customisation features according to our requirements.

For monolithic workloads Google Compute Engine can be used but for microservices where services need to be decoupled, it becomes difficult to use.

In our company, we primarily use Google Compute Engine to create VM instances that would power our website our testing environment and as a CI/CD tool.

Compute Engine is, in my opinion, very well advised if a scalable environment is needed.
Especially for those use cases where incoming traffic might change a lot in a time period.
Is it less appropriate instead if no high traffic is expected.

Most development servers and build machines are migrating into Google Cloud Compute engine. This allows us to spin up/down resources on demand based on workload, product needs, etc. QA does the same for testing resources.

It's Google! Always strong for devs and engineers. Cost, seemingly cheaper than Azure and AWS, yes. In practice, who knows. Their APIs and CLI are strong enough to ensure this is definitely an 8 in recommendation.