What is Amazon EC2?
What is DigitalOcean?
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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.
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.
A VPS gives the full access that I need, because most of what I do has complex integrations and there is plenty of legacy - very stable, highly tuned code developed over two decades - that I carry with me. My use is also limited to during development, so there is no point going for a full server.
Amazon EC2 is a VPS, except it is cheaper.
Additionally, I used to previously take the code developed on my VPS and deploy it to whatever server the client brought.
With Amazon EC2 the deployment is already done. All that remains it to scale up, add other products like dns, mail, storage and so on, and change the billing so that the client gets invoiced. That makes the process that much more predictable and seamless, and the end result much more stable.
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