Google Compute Engine vs WebFaction: What are the differences?
What is Google Compute Engine? 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.
What is WebFaction? Hosting for developers: full shell access to fast servers with all your favorite tools pre-installed and maintained for you. No need to spend hours installing and configuring the software, database and other tools. We have over 50 one-click installers in our control panel.
Google Compute Engine and WebFaction can be primarily classified as "Cloud Hosting" tools.
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, WebFaction provides the following key features:
- 100GB diskspace, RAID-10 setup
- 512MB application RAM
- Free RAM for web and database servers
"Backed by google" is the top reason why over 86 developers like Google Compute Engine, while over 2 developers mention "Cost effective" as the leading cause for choosing WebFaction.
What is Google Compute Engine?
What is WebFaction?
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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.
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 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
Infrastructure for Google App Engine, Google Cloud Endpoints, Memcached, and Google Cloud SQL components, as well as Git repository and Jenkins CI server.