Google Cloud Platform vs Microsoft Azure: What are the differences?
Developers describe Google Cloud Platform as "A suite of cloud computing services". It helps you build what's next with secure infrastructure, developer tools, APIs, data analytics and machine learning. It is a suite of cloud computing services that runs on the same infrastructure that Google uses internally for its end-user products, such as Google Search and YouTube. On the other hand, Microsoft Azure is detailed as "Integrated cloud services and infrastructure to support computing, database, analytics, mobile, and web scenarios". Azure is an open and flexible cloud platform that enables you to quickly build, deploy and manage applications across a global network of Microsoft-managed datacenters. You can build applications using any language, tool or framework. And you can integrate your public cloud applications with your existing IT environment.
Google Cloud Platform and Microsoft Azure belong to "Cloud Hosting" category of the tech stack.
According to the StackShare community, Microsoft Azure has a broader approval, being mentioned in 497 company stacks & 474 developers stacks; compared to Google Cloud Platform, which is listed in 61 company stacks and 24 developer stacks.
What is Google Cloud Platform?
What is Microsoft Azure?
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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.
We are hardcore Kubernetes users and contributors. We loved the automation it provides. However, as our team grew and added more clusters and microservices, capacity and resources management becomes a massive pain to us. We started suffering from a lot of outages and unexpected behavior as we promote our code from dev to production environments. Luckily we were working on our AI-powered tools to understand different dependencies, predict usage, and calculate the right resources and configurations that should be applied to our infrastructure and microservices. We dogfooded our agent (http://github.com/magalixcorp/magalix-agent) and were able to stabilize as the #autopilot continuously recovered any miscalculations we made or because of unexpected changes in workloads. We are open sourcing our agent in a few days. Check it out and let us know what you think! We run workloads on Microsoft Azure Google Kubernetes Engine and Amazon EC2 and we're all about Go and Python!
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.
Windows Azure is more difficult to configure than some other cloud based technologies, however, it makes up for it with the incredible integrations and ease of development on mobile platforms (Android, iOS and of course Windows Phone).
The Azure Web Sites is a PaaS that is very easy to setup and is pretty powerful.
If you want VMs you can have them and even program when they come online.
There are tons of ways to use this service and there are a lot of free things you can get in order to try it out. The only downside is that you have to learn a new, although very powerful, platform.
We use Microsoft Azure because many of our clients are already Azure for their private cloud. Additionally, Azure supports App Service Environments (ASE), which isolates the application resources and gives us a static IP for securely accessing external resources
Additionally, MSSQL supports columnstore tables which is critical for running fast analytics over large datasets
My favourite cloud with all the great tools - web apps, mobile apps, storages, easy tables, blobs, app insights, cosmos DB... I think it is really usable and ergonomic. Plus point for mobile app.
We currently host PRS and EARS on Azure as they are .Net apps, but we are currently porting these services to Scala and will be hosting them on Heroku with the other P2 SRX services.
Serviço utilizado para deploy de toda a infraestrutura do projeto. Colocamos todas as peças do serviço no azure, garantindo uma forma rápida e garantia de escalibilidade.