Overall Satisfaction with Google Compute Engine
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.
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.
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.
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.
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.
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.
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.
- It is easy to use
- It is easy to setup
- Configuration and monitoring of the instances is straightforward and thorough
- The configuration of ingress and egress for the nodes could be easier
- Machine image storing, compression, etc. could be better or have more functionality
- Transferring machine images to and from my local environment is something I have wanted on GCE and its competitors for a long time
- Some of the automated infrastructure deployments we have been able to do on GCE have saved us hassle for our production deployments and allowed us to have periods of downtime for our automated deployment testing environment saving compute resources
- We are able to run several production deployments for several different customer facing products at a comparable cost to what it cost to run one production deployment at a competitor. This is likely due to a combination of the savings mentioned in my earlier point, better pricing on GCP, (and likely Moore's law to some extent where the necessary compute just gets cheaper over time).
- GCP deployments streamline some of the tasks that I do frequently. That's gotta count for something.
I've used the AWS stack, the Azure stack, OCI, and IBM cloud. I have found GCP and AWS the easiest to deploy, configure, manage, and so on. Google has offered me at least 5 free in person classes to train me in new features on the platform. Likewise AWS has offered me probably 10 to 15 guided online tutorials, with a person to field any questions. All of this free training will no doubt affect my experience in a serious way, so keep that in mind as a reader.
Outside of ease of use and configuration, I haven't experienced performance problems across any of the cloud services that I have used.
Outside of ease of use and configuration, I haven't experienced performance problems across any of the cloud services that I have used.
Do you think Google Compute Engine delivers good value for the price?
Yes
Are you happy with Google Compute Engine's feature set?
Yes
Did Google Compute Engine live up to sales and marketing promises?
Yes
Did implementation of Google Compute Engine go as expected?
Yes
Would you buy Google Compute Engine again?
Yes