AWS Lambda is a serverless computing platform that lets users run code without provisioning or managing servers. With Lambda, users can run code for virtually any type of app or backend service—all with zero administration. It takes of requirements to run and scale code with high availability.
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Platform.sh helps companies of all sizes, from SaaS entrepreneurs looking to build, run, and scale their websites and web applications.
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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.
In our organisation we are the only team that uses Platform.sh to host any site. This was a cost effective way for us as we were using Acquia Cloud earlier for these websites. We mostly use Platform.sh for those sites which are always in development as it is simpler and faster to handle these operations in Platform.sh. Then we do a lift and shift to Acquia as we move more towards the go live and post production maintenance side.
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.
Platform.sh is not for beginners in my opinion. It has a good amount of learning curve in my opinion.
As this is a PaaS, teams habituated with cloud infrastructure may miss the server side support from their cloud teams. I believe you will have to work on server bugs more on your own.
During normal maintenance periods, integrations may fail if you are working on your sites in that time, in my experience.
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.
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.
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
In our team we use Platform.sh mostly while sites are in developmental phase. Then we do a lift and shift to either Acquia or AWS depending on the type of sites we have. Platform.sh is really cost effective and more fluid in terms of Continuous Development hence the usage. After said development is done, we generally lift and shift to Acquia for more content heavy sites and to AWS for more transaction oriented sites.
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.