Google Compute Engine

We are using the compute engine to host our web applications frontend and backend, with jaeger for tracing of api calls from backend. We also are using Grafana monitoring tool for monitoring and observability of our gen ai chatbots metric in other vm. The best about it is the ease to create and configure the vm. Configuration of firewall from ui is very easy which is helpful.

Google Compute provides solutions to build dynamic websites and apps for clients without consuming much time. The tool has many features to build a customized apps according to our clients needs. It works on many programming languages and gives us option to build an app based on clients primary requirements.

Compute engine is an offering of GCP, it provides scalable and flexible virtual machine resources for organisations. Business Problem it addresses: Cost-Effectiveness Scalability Performance.

Google Compute Engine provides a high level of customization, flexibility, & scalability in contrast to this, storage is also a major role that it plays. Performance and Security are the Real Factors to go for the GCP Compute Engine

Google Compute Engine (GCE) to host and manage our applications and services in a scalable, reliable, and cost-effective manner. GCE helps us address business challenges related to scalability, reliability, performance, security, and cost-effectiveness. Our use case for GCE includes hosting web applications, running batch processing jobs, supporting machine learning tasks, and more, contributing to our organization's agility and innovation in the cloud.

A scalable and flexible setup to quickly get our apps up and running without dropping tons of cash upfront, paying only for what we actually use with that pay-as-you-go thing and resources automatically scaling to match demand. High availability comes baked in through load balancing across Google's global infrastructure, with security features and custom VM configs letting us tailor things to our app's needs. With growth, it is possible to integrate with Google's full suite of cloud services like analytics, machine learning, serverless, etc. - to keep adding on capabilities while still focusing on building cool stuff instead of dealing with infrastructure headaches. Comparable to AWS EC2.

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.

Our specific department uses google cloud for its data analytics capabilities but also for its scalable and flexible virtual/compute infrastructure. We have some large jobs that we will provision ad-hoc compute to assist as needed. Google Compute Engine excels in this area and has never failed us.

Google Compute Engine is being used across the company for all of the server needs. It lets us create VMs with a variety of customizable options. It does give us a predefined set of VMs that can be directly used based on needs. It also helps us to reduce the cost by using the right size of VMs.

We use Google Compute Engine to support almost all of our in house computer. We have a web application that has a distributed backend of various workloads, all of which run on Compute Engine. Some of these workloads are even containerized. These workloads support the whole organization and provide analysis for research and development.

We use Google Compute Engine across the organization along with other Google Cloud Platform Services. We have interconnected collections of Google Cloud Platform Services which include among these sets of services components deployed on the Google Compute Engine, which act as the backbone of multiple customer facing production deployments.<br><br>To be more precise we have one product which is a web app for monitoring and prediction of household energy consumption and household solar panel power generation. To implement this on GCP we are using numerous GCP services including Google Compute Engine but not limited only to GCE.<br><br>Additionally, we have another product which focuses on voltage optimization primarily for the purpose of conservation voltage reduction at a particular customer site (e.g. a substation controlling a factory, school, large building or facility, etc). The voltage optimization consists primarily of monitoring sensors and performing a near real-time prediction and optimization, and then providing recommended voltage levels for an OLTC controller. This particular deployment must occasionally be deployed on-site, however we also support production level cloud deployment. The cloud deployment will use Google Compute Engine to host a Docker environment, that mirrors the Docker environment that we set up on the machines for the onsite deployments. <br><br>We also use Google Compute engine in a number of prototype builds for testing the efficacy of data science and machine learning models and as a platform for quick collaboration during remote work. Though the number of such deployments are myriad, hence I will forgo the details.<br><br><br><br><br><br><br><br><br>

Google Compute Engine (GCE) is used for most of the AI workload spanning both on-prem private cloud and public cloud. It is used for both onetime training phase for our Deep Learning workload as well as ongoing Deep Learning inference for customer facing applications.

We use Google Compute Engine for staging deployments in the web development department. We are operating off an external assessment that competitor services offer better pricing on production units, so we've agreed to keep Compute Engine in a low tier staging system only. We have yet to do our own audit from an internal efficiency perspective, and how that will impact pricing assessments.

We use compute engine across a number of departments at our company. We deploy temporary workloads to VMs on a daily basis and have deployed our production systems to GCE for deployment and CI/CD pipelines, ETL for data projects, and for large-scale customer engineering.
GCE is in play primarily for our engineering department as well as our customer engineering and sales teams.

The big advantage of using Google Computer Engine is that pricing is much less than if you bought your own hardware. You are only paying for what you need, there's no over-spend required for redundant systems. Google Compute Engine is one of the best IaaS cloud solution at the moment.

Google Compute Engine is used by the DevOps team for hosted Windows servers. This alleviates countless concerns with having a server on-site, and is very comparable to other hosting providers.

We use Google Compute Engine for creating cloud virtual machines that are GPU-accelerated. Because we have centralized most of our backend services to DigitalOcean, we use Google Compute Engine for most of the services that DigitalOcean doesn't offer. Additionally, we use the custom image functionality that GCE offers to create VMs with custom images.

We use Google Compute Engine to host our cloud-based web application. We manage a multi-node, shared instance of our application for thousands of monthly users, as well as individual, dedicated instances for a few of our larger clients. We are a fairly small organization and GCE manages all of our hosting needs.

GCE is very straightforward to use, most of our engineers interact with it on a daily basis. Using GCE means that we can forget about the pains of maintaining computing hardware and just focus on making great software. As a Google Apps user, we also benefit from GCE's rich integration with the rest of the Google product line. Picking GCE over competitors was an easy choice for us.

Compute Engine is a general purpose VM on top of which you can run basically any software you want, including Windows. They are priced on per-hour basis with discounts when you use it for a long time, and also with an option to run a VM for a short time with significant discount, but at a risk that it could be terminated at any time (Preemptible VM) – it's very useful for data processing since you can spawn tens of VMs for cheap and even if they are terminated you can just do it again later.

Google Compute Engine (GCE) is Google's answer to AWS. We use GCE as a low-cost virtual host solution, and we purposefully migrated off AWS and Rackspace to GCE. We made the decision mostly based on cost, but also the flexibility of the toolkit and deployment. GCE is a one-stop shop for hosting virtual and scalable environments in the cloud.

We're a small custom software company with one main web-based program which we host on Google Compute Engine. Over the years we've gained more and more users and we were having issues scaling up as we grew, sometimes slowly and sometimes rapidly. Finally our old hosting solution had a bad down period and we were forced to look elsewhere to get our software back online. Since switching to Compute Engine, we've been able to save money while scaling up as needed. It has been very reliable and overall a perfect fit for us.

It is being used across the whole organization. It addresses the problem of providing solutions to customers who need an all-inclusive infrastructure solution for web services and apps. It has a lot of systems that are managed by Google which people commonly build themselves without knowing that they are reinventing the wheel.

We develop software for our clients and lean on Google Compute Engine and Google Container Engine for hosting those applications. These applications are used both across our clients' organizations as well as publicly by customers of these clients. We made the decision to use Google Compute Engine in order to reduce costs while getting solid reliability from a VPS platform. Google has provided us with both of those needs.

We use Google Compute Engine to support public facing clinical research patient recruitment services, the service is scalable to accommodate increase in capacity due to news items that sign post patients to the services. The platform is IaaS and data is shared with major health charities.

It is being used by a department. It is used to host our school department's website, which is used by both teaching assistants and students.