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Decision at CodeFactor about Google Cloud Functions, Azure Functions, AWS Lambda, Docker, Google Compute Engine, Microsoft Azure, Amazon EC2, CodeFactor.io, Kubernetes, Devops, AI, Machinelearning, Automation, Startup, Autoscale, Containerization, IAAS, SAAS

Avatar of kaskas
Entrepreneur & Engineer
Google Cloud FunctionsGoogle Cloud Functions
Azure FunctionsAzure Functions
AWS LambdaAWS Lambda
Google Compute EngineGoogle Compute Engine
Microsoft AzureMicrosoft Azure
Amazon EC2Amazon EC2

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.

10 upvotes8.2K views

Decision at CodeFactor about .NET, WinForms

Avatar of cordis
Developer at Adroiti

Considering to migrate our analysis engine from .NET Framework to .NET Core. Reported performance improvements look awesome - https://visualstudiomagazine.com/articles/2018/08/22/bing-net-core.aspx. Some of our components use #WinForms, which is not supported in currently-available v2.1. Now we're deciding whether to remove #WinForms entirely and upgrade now or wait for v3.0 which promises support for it (early 2019) - https://blogs.msdn.microsoft.com/dotnet/2018/05/07/net-core-3-and-support-for-windows-desktop-applications

3 upvotes313 views

Decision at CodeFactor about Azure Kubernetes Service

Avatar of cordis
Developer at Adroiti
Azure Kubernetes ServiceAzure Kubernetes Service

We are actively considering to move our infrastructure to Azure Kubernetes Service from our Azure Virtual Machines. Developers don't commit code 24/7. This results in highs and lows where dynamic scaling could help in major way to keep the costs down while providing fast review service.

2 upvotes26 views