Lambda costs are two-fold: You have a set cost per API request as well as a cost per 100ms chunks of work.
On top of this, you can't increase one of CPU or Memory allocation without increasing the other. This means if you're very memory-efficient and CPU-bound, you'll be eating extra runtime costs. You also kind of end up using the entire Amazon toolchain, which has costs embedded in every single bit of it. SNS, SQS, API Gateway, S3 requests, S3 network out, etc they all have costs.
And here's the thing: Lambda has a ton of layering on top of it, which you wouldn't have in an EC2 environment where you have full control. You can't optimize Lambda, you can optimize EC2.
My company is currently paying $4k/mo in Lambda costs, parsing log files in Python into XML and doing an S3 call at a peak of 40 requests / second. Back of the envelope, we can probably get this down to <$300/mo by overprovisioning a few m4.large instances. But now there's the question of having to manage a processing queue, reprocessing, etc so it's hard to tell how much would actually be saved. (On top of that, if a box goes down, that's a significant chunk of processing unable to be taken care of; with Lambda, that doesn't happen).
All in all, has been excellent to us to get started, but there's a point where we definitely want to investigate a dedicated system we have full control of. Lack of Python 3 support was the #1 reason I wanted to do that, so now there's a bit less motivation - it's a lot of work.
I'll certainly write a blog post about it if we decide to move our main processing off Lambda.
On top of this, you can't increase one of CPU or Memory allocation without increasing the other. This means if you're very memory-efficient and CPU-bound, you'll be eating extra runtime costs. You also kind of end up using the entire Amazon toolchain, which has costs embedded in every single bit of it. SNS, SQS, API Gateway, S3 requests, S3 network out, etc they all have costs.
And here's the thing: Lambda has a ton of layering on top of it, which you wouldn't have in an EC2 environment where you have full control. You can't optimize Lambda, you can optimize EC2.
My company is currently paying $4k/mo in Lambda costs, parsing log files in Python into XML and doing an S3 call at a peak of 40 requests / second. Back of the envelope, we can probably get this down to <$300/mo by overprovisioning a few m4.large instances. But now there's the question of having to manage a processing queue, reprocessing, etc so it's hard to tell how much would actually be saved. (On top of that, if a box goes down, that's a significant chunk of processing unable to be taken care of; with Lambda, that doesn't happen).
All in all, has been excellent to us to get started, but there's a point where we definitely want to investigate a dedicated system we have full control of. Lack of Python 3 support was the #1 reason I wanted to do that, so now there's a bit less motivation - it's a lot of work.
I'll certainly write a blog post about it if we decide to move our main processing off Lambda.
Edit: This looks like it has a lot of interesting numbers. https://www.reddit.com/r/Python/comments/4hebys/cost_analysi...