Amazon VPC vs Google Compute Engine: What are the differences?
Amazon VPC: Provision a logically isolated section of the AWS Cloud and launch AWS resources in a virtual network that you define. You have complete control over your virtual networking environment, including selection of your own IP address range, creation of subnets, and configuration of route tables and network gateways. You can easily customize the network configuration for your Amazon VPC; 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.
Amazon VPC can be classified as a tool in the "Virtual Private Cloud" category, while Google Compute Engine is grouped under "Cloud Hosting".
Some of the features offered by Amazon VPC are:
- Create an Amazon Virtual Private Cloud on AWS's scalable infrastructure, and specify its private IP address range from any range you choose.
- Divide your VPC’s private IP address range into one or more public or private subnets to facilitate running applications and services in your VPC.
- Control inbound and outbound access to and from individual subnets using network access control lists.
On the other hand, Google Compute Engine provides the following key features:
- 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.
"Secure" is the primary reason why developers consider Amazon VPC over the competitors, whereas "Backed by google" was stated as the key factor in picking Google Compute Engine.
9GAG, Snapchat, and CircleCI are some of the popular companies that use Google Compute Engine, whereas Amazon VPC is used by Coursera, Intuit, and Expedia.com. Google Compute Engine has a broader approval, being mentioned in 592 company stacks & 428 developers stacks; compared to Amazon VPC, which is listed in 300 company stacks and 79 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.
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
VPC launched in mid 2009 as a companion product to the existing EC2 offering, though it quickly became considered to be EC2 2.0, as it remedied many of the commonly accepted EC2 downfalls. At face value, the migration didn’t seem conceptually difficult, as VPC was just another software abstraction on top of the same hardware, yet it was much more complex, with a few main issues:
- You cannot migrate a running instance.
- AWS offers no migration plan.
- EC2 and VPC do not share security groups.
This last point lingered in our heads as we tried to come up with a solution. What would it take to make EC2 and VPC talk to each other as if the security groups could negotiate? It seemed insurmountable: we had thousands of running instances in EC2 and we could not take any downtime. We were looking for a solution that would allow us to migrate at our own pace, moving partial and full tiers as needed, with secure communication between both sides.
So, we created Neti, a dynamic iptables-based firewall manipulation daemon, written in Python, and backed by Zookeeper.
- 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
Our architecture is running in Amazon VPC. That's actually what we started with and we're still very happy with. We’re pretty much tied into the entire platform.
Infrastructure for Google App Engine, Google Cloud Endpoints, Memcached, and Google Cloud SQL components, as well as Git repository and Jenkins CI server.
The DB and some servers on a separate sub-net in the VPC. This ensures access to these servers are denied from any other machine than the VPC.
With VCP, you can secure and segment your nodes.
It is easy to manage, flexible, and gives great control over your virtual infrastructure.
Build & Deployment tools; Development, Sandbox and Business Continuity environments; ad hoc processing workers