Amazon Elastic Compute Cloud (EC2) vs. AWS Elastic Beanstalk vs. AWS Lambda

Suitable for companies that are looking for performance at a competitive price, flexibility to switch instance type even with RI, flexibility to add-on IOPS, option to lower running cost with the regular introduction of new instance type that comes with higher performance but at a lower cost.

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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.

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Lambda excels at event-driven, short-lived tasks, such as processing files or building simple APIs. However, it's less ideal for long-running, computationally intensive, or applications that rely on carrying the state between jobs. Cold starts and constant load can easily balloon the costs.

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• Huge Diverse range of machine shapes are available which cater to our demand.
• Ability to combine the machines and integrate them with any other service in AWS (Ex: RDS, S3)
• Handling performance and scalability using auto-scaling, ELB configuration and high performance machine shapes.

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• 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.

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• No provisioning required - we don't have to pay anything upfront
• Serverless deployment - it gets executed only when request comes and we pay only for the time the request is getting executed
• Integrates well with AWS CloudWatch triggers so it is easy to setup scheduled tasks like cron jobs

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• The choices on AMIs, instance types and additional configuration can be overwhelming for any non-DevOps person.
• The pricing information should be more clear (than only providing the hourly cost) when launching the instance. AWS DynamoDB gives an estimated monthly cost when creating tables, and I would love to see similar cost estimation showing on EC2 instances individually, as not all developers gets access to the actual bills.
• The term for reserving instances are at least 12 months. With instance types changing so fast and better instances coming out every other day, it's really hard to commit to an existing instance type for 1 or more years at a time.

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• 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.

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• Developing test cases for Lambda functions can be difficult. For functions that require some sort of input it can be tough to develop the proper payload and event for a test.
• For the uninitiated, deploying functions with Infrastructure as Code tools can be a challenging undertaking.
• Logging the output of a function feels disjointed from running the function in the console. A tighter integration with operational logging would be appreciated, perhaps being able to view function logs from the Lambda console instead of having to navigate over to CloudWatch.
• Sometimes its difficult to determine the correct permissions needed for Lambda execution from other AWS services.

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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.

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You an start using EC2 instances immediately, is so easy and intuitive to start using them, EC2 has wizard to create the EC2 instances in the web browser or if you are code savvy you can create them with simple line in the CLI or using an SDK. Once you are comfortable using EC2, you can even automate the process.

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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

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I give it a seven is usability because it's AWS. Their UI's are always clunkier than the competition and their documentation is rather cumbersome. There's SO MUCH to dig through and it's a gamble if you actually end up finding the corresponding info if it will actually help. Like I said before, going to google with a specific problem is likely a better route because AWS is quite ubiquitous and chances are you're not the first to encounter the problem. That being said, using SAM (Serverless application model) and it's SAM Local environment makes running local instances of your Lambdas in dev environments painless and quite fun. Using Nodejs + Lambda + SAM Local + VS Code debugger = AWESOME.

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AWS's support is good overall. Not outstanding, but better than average. We have had very little reason to engage with AWS support but in our limited experience, the staff has been knowledgeable, timely and helpful. The only negative is actually initiating a service request can be a bit of a pain.

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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.

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Amazon consistently provides comprehensive and easy-to-parse documentation of all AWS features and services. Most development team members find what they need with a quick internet search of the AWS documentation available online. If you need advanced support, though, you might need to engage an AWS engineer, and that could be an unexpected (or unwelcome) expense.

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- 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.

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Amazon EC2 is super flexible compared to the PaaS offerings like Heroku Platform and Google App Engine since with Amazon EC2, we have access to the terminal. In terms of pricing, it's basically just the same as Google Compute Engine. The deciding factor is Amazon EC2's native integration with other AWS services since they're all in the same cloud platform.

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.

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AWS Lambda is good for short running functions, and ideally in response to events within AWS. Google App Engine is a more robust environment which can have complex code running for long periods of time, and across more than one instance of hardware. Google App Engine allows for both front-end and back-end infrastructure, while AWS Lambda is only for small back-end functions

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• It reduced the need for heavy on-premises instances. Also, it completely eliminates maintenance of the machine. Their SLA criteria are also matching business needs. Overall IAAS is the best option when information is not so crucial to post on the cloud.
• It makes both horizontal and vertical scaling really easy. This keeps your infrastructure up and running even while you are increasing the capacity or facing more traffic. This leads to having better customer satisfaction.
• If you do not choose your instance type suitable for your business, it may incur lots of extra costs.

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• till now we had not Calculated ROI as the project is still evolving and we had to keep on changing the environment implementation
• it meets our purpose of quick deployment as compared to on-premises deployment
• till now we look good as we also controlled our expenses which increased suddenly in the middle of deployment activity

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• Positive - Only paying for when code is run, unlike virtual machines where you pay always regardless of processing power usage.
• Positive - Scalability and accommodating larger amounts of demand is much cheaper. Instead of scaling up virtual machines and increasing the prices you pay for that, you are just increasing the number of times your lambda function is run.
• Negative - Debugging/troubleshooting, and developing for lambda functions take a bit more time to get used to, and migrating code from virtual machines and normal processes to Lambda functions can take a bit of time.

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