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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.
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.
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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 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.
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 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
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.
My primary OS is LINUX, Linode gives me everything I need in hosting service.
Enough RAM for the price, high speed network and CPUs.
- 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
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.
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.
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.
Infrastructure for Google App Engine, Google Cloud Endpoints, Memcached, and Google Cloud SQL components, as well as Git repository and Jenkins CI server.
DigitalOcean is the backbone for the whole stack. Without its cheap pricing and easy scalability this whole project wouldn't be possible.