Google Compute Engine vs OpenShift: What are the differences?
Developers describe Google Compute Engine as "Run large-scale workloads on virtual machines hosted on Google's infrastructure". Google Compute Engine is a service that provides virtual machines that run on Google infrastructure. Google Compute Engine offers scale, performance, and value that allows you to easily launch large compute clusters on Google's infrastructure. There are no upfront investments and you can run up to thousands of virtual CPUs on a system that has been designed from the ground up to be fast, and to offer strong consistency of performance. On the other hand, OpenShift is detailed as "Red Hat's free Platform as a Service (PaaS) for hosting Java, PHP, Ruby, Python, Node.js, and Perl apps". OpenShift is Red Hat's Cloud Computing Platform as a Service (PaaS) offering. OpenShift is an application platform in the cloud where application developers and teams can build, test, deploy, and run their applications.
Google Compute Engine belongs to "Cloud Hosting" category of the tech stack, while OpenShift can be primarily classified under "Platform as a Service".
Some of the features offered by Google Compute Engine are:
- High-performance virtual machines- Compute Engine’s Linux VMs are consistently performant, scalable, highly secure and reliable. Supported distros include Debian and CentOS. You can choose from micro-VMs to large instances.
- Powered by Google’s global network- Create large compute clusters that benefit from strong and consistent cross-machine bandwidth. Connect to machines in other data centers and to other Google services using Google’s private global fiber network.
- (Really) Pay for what you use- Google bills in minute-level increments (with a 10-minute minimum charge), so you don’t pay for unused computing time.
On the other hand, OpenShift provides the following key features:
- Built-in support for Node.js, Ruby, Python, PHP, Perl, and Java (the standard in today's Enterprise)
- OpenShift is extensible with a customizable cartridge functionality that allows developers to add any other language they wish. We've seen everything from Clojure to Cobol running on OpenShift.
- OpenShift supports frameworks ranging from Spring, to Rails, to Play
"Backed by google" is the primary reason why developers consider Google Compute Engine over the competitors, whereas "Good free plan" was stated as the key factor in picking OpenShift.
OpenShift is an open source tool with 915 GitHub stars and 563 GitHub forks. Here's a link to OpenShift's open source repository on GitHub.
According to the StackShare community, Google Compute Engine has a broader approval, being mentioned in 592 company stacks & 428 developers stacks; compared to OpenShift, which is listed in 50 company stacks and 52 developer stacks.
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CodeFactor being a #SAAS product, our goal was to run on a cloud-native infrastructure since day one. We wanted to stay product focused, rather than having to work on the infrastructure that supports the application. We needed a cloud-hosting provider that would be reliable, economical and most efficient for our product.
CodeFactor.io aims to provide an automated and frictionless code review service for software developers. That requires agility, instant provisioning, autoscaling, security, availability and compliance management features. We looked at the top three #IAAS providers that take up the majority of market share: Amazon's Amazon EC2 , Microsoft's Microsoft Azure, and Google Compute Engine.
AWS has been available since 2006 and has developed the most extensive services ant tools variety at a massive scale. Azure and GCP are about half the AWS age, but also satisfied our technical requirements.
It is worth noting that even though all three providers support Docker containerization services, GCP has the most robust offering due to their investments in Kubernetes. Also, if you are a Microsoft shop, and develop in .NET - Visual Studio Azure shines at integration there and all your existing .NET code works seamlessly on Azure. All three providers have serverless computing offerings (AWS Lambda, Azure Functions, and Google Cloud Functions). Additionally, all three providers have machine learning tools, but GCP appears to be the most developer-friendly, intuitive and complete when it comes to #Machinelearning and #AI.
The prices between providers are competitive across the board. For our requirements, AWS would have been the most expensive, GCP the least expensive and Azure was in the middle. Plus, if you #Autoscale frequently with large deltas, note that Azure and GCP have per minute billing, where AWS bills you per hour. We also applied for the #Startup programs with all three providers, and this is where Azure shined. While AWS and GCP for startups would have covered us for about one year of infrastructure costs, Azure Sponsorship would cover about two years of CodeFactor's hosting costs. Moreover, Azure Team was terrific - I felt that they wanted to work with us where for AWS and GCP we were just another startup.
In summary, we were leaning towards GCP. GCP's advantages in containerization, automation toolset, #Devops mindset, and pricing were the driving factors there. Nevertheless, we could not say no to Azure's financial incentives and a strong sense of partnership and support throughout the process.
Bottom line is, IAAS offerings with AWS, Azure, and GCP are evolving fast. At CodeFactor, we aim to be platform agnostic where it is practical and retain the flexibility to cherry-pick the best products across providers.
Heroku vs OpenShift. I've never decided which one is better. Heroku is easier to configure. Openshift provide a better machine for free. Heroku has many addons for free. I've chosen Heroku because of easy initial set-up. I had deployment based on git push. I also tried direct deployment of jar file. Currently Heroku runs my Docker image. Heroku has very good documentation like for beginners. So if you want to start with something, let's follow Heroku. On the other hand OpenShift seems like a PRO tool supported by @RedHat.
Google Compute Engine Amazon Web Services OVH Microsoft Azure Go GitHub
Last week, we released a fresh new release of Komiser with support of multiple AWS accounts. Komiser support multiple AWS accounts through named profiles that are stored in the credentials files.
You can now analyze and identify potential cost savings on unlimited AWS environments (Production, Staging, Sandbox, etc) on one single dashboard.
Read the full story in the blog post.
Google Compute Engine Amazon Web Services Go Docker Material Design for Angular Microsoft Azure GitHub I’m super excited to annonce the release of Komiser:2.1.0 with beta support of Google Cloud Platform. You can now use one single open source tool to detect both AWS and GCP overspending.
Komiser allows you to analyze and manage #cloud cost, usage, #security, and governance in one place. Hence, detecting potential vulnerabilities that could put your cloud environment at risk.
It allows you also to control your usage and create visibility across all used services to achieve maximum cost-effectiveness and get a deep understanding of how you spend on the #AWS, #GCP and #Azure.
I use Google Compute Engine instances as flexible, reproducible infrastructure that scale with my data science tasks.
Between Google Cloud and Amazon Web Services, I chose Google Cloud for its intuitive UI. SSH within the browser is very convenient.
Related blog post with example usage: Running an IPython Notebook on Google Compute Engine from Chrome
I needed a PaaS provider that didn't drop all the time and it's hard to find a good option for PHP applications. Openshift takes care of it. They are a little behind on PHP versions, but that can be solved with a custom cartridge. It requires a little more elbow grease to get rolling when you want to implement something they don't already have, but their quickstarts are great to get rolling with the basics quickly.
- I use Google Compute Engine instances as flexible, reproducible infrastructure that scales with my data science tasks.
- Between Google Cloud and Amazon Web Services, I chose Google Cloud for its intuitive UI. SSH within the browser is very convenient.
- Related blog post with example usage: Running an IPython Notebook on Google Compute Engine from Chrome
SG-TravelBuddy server application is hosted on Red Hat OpenShift Online (v3). SG-TravelBuddy mobile (Android) app is connecting to this server for data operations.
Infrastructure for Google App Engine, Google Cloud Endpoints, Memcached, and Google Cloud SQL components, as well as Git repository and Jenkins CI server.