Google App Engine vs Google Compute Engine: What are the differences?
Google App Engine: Build web applications on the same scalable systems that power Google applications. Google has a reputation for highly reliable, high performance infrastructure. With App Engine you can take advantage of the 10 years of knowledge Google has in running massively scalable, performance driven systems. App Engine applications are easy to build, easy to maintain, and easy to scale as your traffic and data storage needs grow; 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.
Google App Engine can be classified as a tool in the "Platform as a Service" category, while Google Compute Engine is grouped under "Cloud Hosting".
Some of the features offered by Google App Engine are:
- Zero to sixty: Scale your app automatically without worrying about managing machines.
- Supercharged APIs: Supercharge your app with services such as Task Queue, XMPP, and Cloud SQL, all powered by the same infrastructure that powers the Google services you use every day.
- You're in control: Manage your application with a simple, web-based dashboard allowing you to customize your app's performance.
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.
"Easy to deploy", "Auto scaling" and "Good free plan" are the key factors why developers consider Google App Engine; whereas "Backed by google", "Easy to scale" and "High-performance virtual machines" are the primary reasons why Google Compute Engine is favored.
9GAG, Snapchat, and CircleCI are some of the popular companies that use Google Compute Engine, whereas Google App Engine is used by Snapchat, Accenture, and Movielala. Google Compute Engine has a broader approval, being mentioned in 594 company stacks & 429 developers stacks; compared to Google App Engine, which is listed in 482 company stacks and 345 developer stacks.
What is Google App Engine?
What is Google Compute Engine?
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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
With Cloud Endpoints you can create and deploy mobile backend in one hour or less. And it is free (until you need extra scale). I would not recommend to use Java - python is faster and has all new appengine features.
Pros: everything is in one place: task queue, cron, backend instances for data processing, datastore, mapreduce. Cons: you cannot easily move your code from GAE. Even with special 3rd party services.
With Cloud Endpoints you can create and deploy mobile backend in one hour or less.
PaaS for back-end components, including external data ingestion APIs, front-end web service APIs, hosting of static front-end application assets, back-end data processing pipeline microservices, APIs to storage infrastructure (Cloud SQL and Memcached), and data processing pipeline task queues and cron jobs. Task queue fan-out and auto-scaling of back-end microservice instances provide parallelism for high velocity data processing.
- 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
checking a swap require a lot of cpu resource, roster normally come out same day of month, every month, at a particular time. Which make very high spike, our flag ship product, iSwap, with the capability looking swap possibility with 10000 other rosters base on user critieria, you need a cloud computing give you this magnitude of computing power. gae did it nicely, user friendly, easy to you, low cost.
Infrastructure for Google App Engine, Google Cloud Endpoints, Memcached, and Google Cloud SQL components, as well as Git repository and Jenkins CI server.
App engine fills in the gaps in the increasingly smaller case where it's necessary for us to run our own APIs.
Very easy to make cloud computing of ML models , and use containers like Kubernetes.
Cloud instances to run our app, Cloud MySQL , Network Load Balancer