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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!
I use DigitalOcean because of the simplicity of using their basic offerings, such as droplets. In AppAttack, we need low-level control of our infrastructure so we can rapidly deploy a custom training web application on-demand for each training session, and building a Kubernetes cluster on top of DigitalOcean droplets allowed us to do exactly that.
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.
For those needing hosting on Windows or Windows Server too (and avoiding licensing hurdles), both Vultr and Amazon LightSail offer compelling choices, depending on how much compute power you need. Don't underestimate Amazon LightSail, especially for smaller or starting projects, but Vultr also offers an incremental $16 Windows option on top of their standard compute offerings.
I started using DigitalOcean back in January to host a Ghost blog. I was a little worried at first because I didn't have too much experience setting up servers. There was always the option of a full service company that does all the work for you, but the point was that I wanted more control for the purpose of learning. And, learning turned out to be really easy thanks to the great community at DigitalOcean. There are tutorials for just about anything. It has been an amazing learning experience, and now I'm looking forward to hosting more complex projects here. I already have a couple in the works for the near future. I highly recommend it.
I can't rate the Support great or bad, as I haven't really had a need to contact them yet. But everything else has been excellent so far.
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.
I am a freelancer and a researcher. I have had tried a lot of hosting services over the years. But DIgitalOcean stands out from the rest for its pricing. Its just five dollar a month for a basic node.
And the other reason for loving Digital Ocean is that they support Docker. It you buy a VPS machine, chances are that docker support wont be available as with PV or hypervisor, docker need some extra config.
So far I am loving DO :-)
I use DigitalOcean for testing or hosting my apps. You can set up an Ubuntu server in less than a minute. There are also one-click-install apps, so I don't have to install e.g. the LAMP stack myself. The dashboard has a really easy UI and is easy to use. The costs begin at 5 bucks per month. Also DigitalOcean has a great support and an adorable community. They have a great support page with hundreds of tutorials.
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
DigitalOcean offers us everything we need to test out specific scenarios or we expect from small-servers like our monitoring-system. We also use digital-ocean in long-term and are very satisfied with their performance and scalability.
Because I like having more control of my deployment, I am currently hosting this on DigitalOcean. I don't need to worry about arbitrary row limits and I can be sure that the app is always running.
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.
We use DigitalOcean to host our build tools (namely Drone.io) for a cheap CI and CD server.
We'll be using this to host the server application during alpha phase.
Blackbaud makes use of Azure and my current job is with Blackbaud. Therefore, due to the free credit and the ability to reuse tools, I rely on Azure quite a bit.
Been hosting with them for a while now. Never had an issue, great support, great docs: can't beat 'em. Though I'd probably move to AWS for large scale projects.