AWS Elastic Beanstalk is the platform-as-a-service offering provided by Amazon and designed to leverage AWS services such as Amazon Elastic Cloud Compute (Amazon EC2), Amazon Simple Storage Service (Amazon S3).
$35
per month
Google Cloud Functions
Score 9.1 out of 10
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Google Cloud Functions enables users to run code in the cloud with no servers or containers to manage. Cloud Functions is a scalable, pay-as-you-go functions as a service (FaaS) product to help build and connect event driven services with simple, single purpose code.
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Pricing
AWS Elastic Beanstalk
Google Cloud Functions
Editions & Modules
No Charge
$0
Users pay for AWS resources (e.g. EC2, S3 buckets, etc.) used to store and run the application.
There are many services like AWS Elastic beanstalk, but there are none with the maturity in the platform or the cost-effectiveness of AWS Elastic Beanstalk. Also, AWS Elastic Beanstalk is the oldest among them, so there are more people with AWS experience than the other …
I have been using AWS Elastic Beanstalk for more than 5 years, and it has made our life so easy and hassle-free. Here are some scenarios where it excels -
I have been using different AWS services like EC2, S3, Cloudfront, Serverless, etc. And Elastic Beanstalk makes our lives easier by tieing each service together and making the deployment a smooth process.
N number of integrations with different CI/CD pipelines make this most engineer's favourite service.
Scalability & Security comes with the service, which makes it the absolute perfect product for your business.
Personally, I haven't found any situations where it's not appropriate for the use cases it can be used. The pricing is also very cost-effective.
It is easy to use, in 15 minutes you just have to follow a few steps, do some easy configurations and you have the project ready to run, once it is connected to the codebase, the execution is automatic. For anyone coming into the google environment, Functions make code execution easy and transparent. CI/CD is perfect
Getting a project set up using the console or CLI is easy compared to other [computing] platforms.
AWS Elastic Beanstalk supports a variety of programming languages so teams can experiment with different frameworks but still use the same compute platform for rapid prototyping.
Common application architectures can be referenced as patterns during project [setup].
Multiple environments can be deployed for an application giving more flexibility for experimentation.
Limited to the frameworks and configurations that AWS supports. There is no native way to use Elastic Beanstalk to deploy a Go application behind Nginx, for example.
It's not always clear what's changed on an underlying system when AWS updates an EB stack; the new version is announced, but AWS does not say what specifically changed in the underlying configuration. This can have unintended consequences and result in additional work in order to figure out what changes were made.
As our technology grows, it makes more sense to individually provision each server rather than have it done via beanstalk. There are several reasons to do so, which I cannot explain without further diving into the architecture itself, but I can tell you this. With automation, you also loose the flexibility to morph the system for your specific needs. So if you expect that in future you need more customization to your deployment process, then there is a good chance that you might try to do things individually rather than use an automation like beanstalk.
The overall usability is good enough, as far as the scaling, interactive UI and logging system is concerned, could do a lot better when it comes to the efficiency, in case of complicated node logics and complicated node architectures. It can have better software compatibility and can try to support collaboration with more softwares
Overall Google Cloud Functions is losing a lot of benefits to other GCP services, making it less attractive to users. A simple example would be the need to zip application files and push them to Google Storage which makes it a bit complicated to automate via a CI/CD pipeline. Another "similar" solution would be using Cloud Run although the need for a docker image is there, with the recent evolutions to Cloud Run (ability to downscale to 0) it makes a lot more interesting.
As I described earlier it has been really cost effective and really easy for fellow developers who don't want to waste weeks and weeks into learning and manually deploying stuff which basically takes month to create and go live with the Minimal viable product (MVP). With AWS Beanstalk within a week a developer can go live with the Minimal viable product easily.
Documentation is provided and clear for this service. Although GCP support is included in the current contract we didn't get to use it since the process is pretty straightforward.
- Do as many experiments as you can before you commit on using beanstalk or other AWS features. - Keep future state in mind. Think through what comes next, and if that is technically possible to do so. - Always factor in cost in terms of scaling. - We learned a valuable lesson when we wanted to go multi-region, because then we realized many things needs to change in code. So if you plan on using this a lot, factor multiple regions.
We also use Heroku and it is a great platform for smaller projects and light Node.js services, but we have found that in terms of cost, the Elastic Beanstalk option is more affordable for the projects that we undertake. The fact that it sits inside of the greater AWS Cloud offering also compels us to use it, since integration is simpler. We have also evaluated Microsoft Azure and gave up trying to get an extremely basic implementation up and running after a few days of struggling with its mediocre user interface and constant issues with documentation being outdated. The authentication model is also badly broken and trying to manage resources is a pain. One cannot compare Azure with anything that Amazon has created in the cloud space since Azure really isn't a mature platform and we are always left wanting when we have to interface with it.
It is easier to keep everything in house when we are using GCP or AWS. To mix Lambda with google cloud is not a best practice and will cause problems ahead. The segmentation is clear, if you are using google, you use Google Cloud Functions. if you are on Amazon, you use all AWS tools. You can't mix them. The price is set.
