Go into your personal health dashboard. The notification should show the impacted functions. Once you find the function(s) you need to update to a newer python runtime. That could be as easy as just changing the runtime version in the console, but you also may need to make code changes More on reddit.com

the thing to keep in mind with lambda is you don't get a full vcpu of compute until your function memory is 1,769mb or higher (https://docs.aws.amazon.com/lambda/latest/dg/configuration-memory.html). if your function would benefit from multiple cores, i.e. is multithreaded with parallel busy threads, you'll need to scale up multiples of that to get more vcpus.

other than that, lambda runs on ec2 so the premise of your question is a little weird. runtime choice affects cold start time and how much memory you'll need just for overhead. other than that it has nothing to do with efficiency.

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To clarify a couple of things: AWS Lambda is one of many ways to deploy code on AWS. AWS Lambda ECS EC2 EKS etc... You can deploy almost any type of code on all of these including a python API. What makes AWS Lambda special is: You can deploy docker containers, or .zip files. LINK It is 100% "serverless", AKA AWS manages the server for you, and you just tell AWS how many instances you want to run. This is done with Firecracker LINK It can scale from 0-10,000+ instances really quickly It can be very cheap vs a EC2 instance if you have a bursty load You can string together multiple Lambda functions using AWS Step Functions LINK You can trigger the lambda functions with a wide range of things. LINK You can run numerous functions in parallel. Stateless (ish, you can have state, but don't do that) This is both positive and negative. Most modern code should be stateless. IE, you should be able to kill it at any point, and run it again. The state should be stored in a database, not locally with the code. AWS Lambda has some downsides: It can be expensive if you have a constant load Data processing, 5+ minute loads, etc. Most light/fast APIs will be much cheaper on Lambda than a tradition ECS + EC2 setup. Testing locally is annoying LINK Lambda does some weird things with requests. You need to add some middleware to handle it. LINK Cold starts! If you haven't called your Lambda in ~15 minutes, the next call can take much longer to start. LINK You don't have control, so if something goes wrong with AWS Lambda, you can't do anything about it. 15 minute max You can only keep a lambda up for a max of 15 minutes. For an API this is more than enough, but for a larger job you should look somewhere else (ECS Fargate, AWS Batch) Other limits (CPU, Time, RAM, etc.) LINK Other, more “traditional” ways to deploy a web API would be AWS ECS or AWS EC2. ECS offers something called Fargate. It is built on the same serverless framework (Firecracker) and will let you deploy code without dealing with the underlying hardware. This has a lot of the same advantages as AWS Lambda without most of the disadvantages. Now to the question I suspect you are really asking. "When should I use a Web API vs another processing method like a queue consumer, map reduce job, ETL pipeline, batch job, etc?" That is a very hard question to answer, and it depends on exactly what you are trying to do. Based on your post history, I assume you are trying to move a monolithic ERP system to microservices? EDIT: Sorry about the spam edits, I decided to move to my computer and give a better answer. More on reddit.com

I've written probably 50 lambdas in Python at this point and to be honest keeping it simple is the best way. Follow PIP standards to keep your code readability high, use the paginator objects boto3 provides where possible and try to keep the execution time down.15 minutes is pretty high for something serverless so I tend to design my lambdas to perform a single task and then use something like AWS Step Functions to orchestrate several in a flow or in Map states (loops). I also make use of environment variables where possible and template a lot of functions for common things like assuming roles. I also make use of comprehension but don't go too overboard with that because it can reduce readability if you've got a fat one defined haha. No need to add comments every other line, just a single line docstring at the top of the function should be enough to describe it. There's also some neat docker commands out there to build all your packages into a single lambda layer using a requirements.txt, using the lambda image, and this should be possible to do with Terraform, or if you've only got one or two packages just prepare it in a CI/CD and use terraform to build and apply the layer. More on reddit.com