I'm transitioning to Render from heroku. The pricing scale matches my usage scale, yet it's just as easy to deploy. It's removed a lot of the devops that I don't like to deal with on setting up my own raw *nix box and makes deployment simple and easy!

Clustering I don't use clustering features at the moment but when i need to set up clustering of nodes and discoverability, render will enable that where Heroku would require that I use an external service like redis.

Restarts The restarts are annoying. I understand the reasoning, but I'd rather watch my service if its got a memory leak and work to fix it than to just assume that it has memory leaks and needs to restart.

I host my stuff with Hetzner in large part because their tools are straighforward and easy to use and they're based in a country whose laws respect privacy.

If you're hosting with a company that's US-based, you have to worry about their laws affecting your site, which isn't an acceptable requirement in my opinion.

DigitalOcean was where I began; its USD5/month is extremely competitive and the overall experience as highly user-friendly.

However, their offerings were lacking and integrating with other resources I had on AWS was getting more costly (due to transfer costs on AWS). Eventually I moved the entire project off DO's Droplets and onto AWS's EC2.

One may initially find the cost (w/o free tier) and interface of AWS daunting however with good planning you can achieve highly cost-efficient systems with savings plans, spot instances, etcetera.

Do not dive into AWS head-first! Seriously, don't. Stand back and read pricing documentation thoroughly. You can, not to the fault of AWS, easily go way overbudget. Your first action upon getting your AWS account should be to set up billing alarms for estimated and current bill totals.

We first selected Google Cloud Platform about five years ago, because HIPAA compliance was significantly cheaper and easier on Google compared to AWS. We have stayed with Google Cloud because it provides an excellent command line tool for managing resources, and every resource has a well-designed, well-documented API. SDKs for most of these APIs are available for many popular languages. I have never worked with a cloud platform that's so amenable to automation. Google is also ahead of its competitors in Kubernetes support.

(2/3) Numpy: This is a package for scientific computing in python. Vectorised code from this library also makes this very efficient. This is an absolutely essential library for machine learning and will help us develop our models efficiently. SciKit-Learn, our machine learning library of choice, uses Numpy extensively and therefore it has to be used by us.

SciKit-Learn: SciKit-Learn is a machine learning library in python. It includes models for regression, classification, clustering, SVM’s, forests, etc. We have chosen this because of all the types of models it offers and the ease with which they can be integrated with our software.

Stripe: POS system of choice with direct integration to all major forms of payment. By using stripe, we can worry less about payment security as they are innovators in the industry and are dedicated to making POS reliable, fast, and scalable.

Firebase: Direct integration into Python makes this NoSQL database a perfect choice. Search is fast and uses an indexing method similar to Google allowing for low latency. Also has the benefit of allowing context casting to remodel JSON data into workable python objects easily.

Heroku: Heroku integrates really well with GitHub and makes the process of deploying our web app extremely easy. After setting up Heroku on our GitHub project, a simple push to it’s master branch deploys the updates. It’s really simple to set up and uses and it is absolutely free. Furthermore, it makes the web app accessible to everyone instead of deploying it locally which is a huge plus.

GCE is much more user friendly than EC2, though Amazon has come a very long way since the early days (pre-2010's). This can be seen in how easy it is to edit the storage attached to an instance in GCE: it's under the instance details and is edited inline. In AWS you have to click the instance > click the storage block device (new screen) > click the edit option (new modal) > resize the volume > confirm (new model) then wait a very long time. Google's is nearly instant.

• In both cases, the instance much be shut down.

There also the preference between "user burden-of-security" and automatic security: AWS goes for the former, GCE the latter.

React is used for its functional nature and simplification of ES6. The efficiency of how it re-renders based on components is an added bonus. styled-components is used to simplify CSS 3 styling, and to avoid having additional files.

The Node.js framework, ExpressJS, is used for the back-end to create a RESTful API for the NoSQL Firebase database.

Deployment is done through Heroku. ExpressJS, Firebase, and Heroku are used for their ease of use and simplicity, over its alternatives for this proof of concept application.

They would most likely be migrated into Laravel, aws, and MySQL, for production because of their scalability, and robustness if used with a larger dataset and userbase.

Finally, Visual Studio Code is used as a code editor, due to its combination of features and speed, while also having an integrated terminal.

Most bioinformatics shops nowadays are hosting on AWS or Azure, since they have HIPAA tiers and offer enterprise SLA contracts. Meanwhile Heroku hasn't historically supported HIPAA. Rackspace and Google Cloud would be other hosting providers we would consider, but we just don't get requests for them. So, we mostly focus on AWS and Azure support.