Google Compute Engine

One thing I really love about Google Compute Engine is the inbuilt security features such as virtual private clouds (VPCS), firewalls and encryption, which helps us by protecting our data and applications from unauthorised access and cyber threats. Also the cost efficiency as this tool offers pay-as-you-go pricing model, which allows us to pay for the resources we use makes it very unique and highly recommended cloud engine platform. Google Compute Engine is not well suited for the non-technical users because it might be difficult navigate through all the features as they're more technical.

Google Compute Engine is so easy to implement and run. It doesn't require much knowledge to build an app since they provide multiple options to choose from with their prebuilt sample list. We can easily make customization on any website app we built for our client according to their needs and make changes if required.

Google Compute Engine (GCE) is well suited adding cloud storage. GCE provides excellent block storage capabilities and is well suited for adding addition storage to the application. The applications can be globally load balanced across different regions thus maintaining high availability and response times. It supports VMs of different operating systems thus enabling correct infrastructural choices. GCE UI is not very user friendly can can be improved. Also the networking setup is not very intuitive.

I will recommend the Compute Engine if we are concerned of Security, Flexibility and Scalabilty but, It can be improved a bit more in terms of Ease of Usage as compared to AWS EC2 or Azure VM.

Suited -Batch Processing and Data Analytics, Web Application Hosting, Containerized Workloads, high performance computing
Unsuited - low traffic websites, static websites, legacy applications, small scale web apps

Hosting dynamic websites where traffic fluctuates - easy scaling. Running compute-heavy backend processes, like data analysis, to benefit from customizable VM sizes. Creating dev and testing environments quickly. Can be overkill for some solutions.

Google Compute Engine instances are an easy way to quickly run your applications with good performance, security, and functionality.

With Google Compute Engine instances, you don't need to worry much about machine maintenance and upgrades, and instance monitoring is easy and clear since Google provides cool dashboards to check machine status and notify you about required updates or changes.

I would still recommend Google Compute Engine for application build and testing but not for building SaaS. As it'd be more tricky to integrate any third party apps, as Google already provides most of the services but sometime our clients request for such customisations, which is more suited per their internal alignments.

Machine Learning is a tool which is more efficient than any other provider and has wide range of languages for processing.

If your startup has a web app or SaaS offering that needs to scale quickly based on user demand, Google Compute Engine's auto-scaling capabilities make it a great fit. You can easily spin up more VMs during traffic spikes without overprovisioning resources. For data-intensive workloads like big data processing or training machine learning models, Google Compute Engine's flexible compute instances with GPU support can provide cost-effective scalability. If you have a short-term project or MVP with minimal scaling needs, the overhead of setting up Google Compute Engine may not be worth it.

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.

GCE is cost effective when compared to other cloud provider solutions. I would recommend a colleague GCE. When I ran same performance test on aws ec2 and Google GCE i got better numbers in GCE. It could be related our product but for performance tests i prefer using GCE.

I would say that it is very helpful for scenarios with iregular processing power requirements, as situations where the application have burst of usage, or that have demands that change during the year or between the seasons. On the other hand, applications that have a very stable load can easily become too expensive for cloud usage.

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.

If you have a prospective client who is currently using MS PowerBI, document security will be a really big deal. We are in the middle of trying to find a fixed gap for one of our government prospects. If secure documents aren't an issue for them, then it is fine.

Google Compute Engine will be suited for the scenarios with the quota limit provided as default and less appropriate for the scenarios on deployments with quota limit increase request need to raise to Google Cloud support.

it is well suited for general compute, we use it more as a back up solution. In most workloads, it seems to be handling things just as it should.

Works very well when you need scalability and quick infrastructure. As you start to scale it is important to evaluate other cloud services available to determine if there are greater advantages within the Google cloud platform offerings.

We use Google Compute Engine in a hybrid and multi-cloud solution. We find that using it for direct ad-hoc use cases meets all of our demands. We have attempted some more complex networking and multi-regional use cases but were not able to achieve satisfactory results. Google Compute Engine is extremely appropriate for anyone requiring quick, scalable, reliable infrastructure.

GCE is well suited for the following scenarios. GCE is very easy to use, and we can navigate on the console easily for trying various options. Reasonable pricing for the VMs help to reduce the overall budget. Recommendations for running VMs to scale up or down. Set of predefined VM types. Preemptible instances.

Google Compute Engine is suitable for any scenario that fits one of the pros I listed. It is very flexible and easily configurable. In many cases, VMs need to be spun up on the fly programmatically, and without orchestration software, one can accomplish this by using one of the APIs.

I have recommended the platform to students, friends and family members alike. The help and documentation is very easy to follow for a beginner. Plus, GCP has built in tools which make some common tasks that non-production level cloud users need to accomplish very easy in an automated way.

Now, to address the question of recommending GCE to a colleague, ultimately the organization will have to make a decision regarding the entire cloud platform. It wouldn't make much sense, outside of a special case, to use GCE for some parts of your cloud infrastructure and a competitor on other parts.

That practical caveat aside, I believe that the GCP brings a strong suite of tools to the table overall and is good value for money at this time as well.

Developer familiarity to certain competing platforms can be a sticking point, but a colleague who is already asking for a recommendation is likely already open minded about moving to GCP.

It is a stable service created by Google. The quality of service is good enough to create a full fledged production environment.

Google Compute Engine is particularly well suited for multi-environment testing, development, and experimentation with the low cost of spinning up small instances along with the speed at which it can be done. It also does well hosting small websites and proxy servers that don't see a lot of network traffic.

GCE is very well suited to small to mid scale deployments. It is also run very well with AI workloads especially when using Google TenserFlow et al. It is less appropriate for extremest scale deployments that spam multiple data center (probably because of lack of document on best practices)