Ask HN: How was your experience with AWS Lambda in production?

Will check that out, cheers!

Or you can use CI to deploy.

I've been using AWS Lambda on a side project (octodocs.com) that is powered by Django and uses Zappa to manage deployments.

I was initially attracted to it as a low-cost tool to run a database (RDS) powered service side project.

Some thoughts:

- Zappa is a great tool. They added async task support [1] which replaced the need for celery or rq. Setting up https with let's encrypt takes less than 15 minutes. They added Python 3 support quickly after it was announced. Setting up a test environment is pretty trivial. I set up a separate staging site which helps to debug a bunch of the orchestration settings. I also built a small CLI [2] to help set environment variables (heroku-esque) via S3 which works well. Overall, the tooling feels solid. I can't imagine using raw Lambda without a tool like Zappa.

- While Lambda itself is not too expensive, AWS can sneak in some additional costs. For example, allowing Lambda to reach out to other services in the VPC (RDS) or to the Internet, requires a bunch of route tables, subnets and a nat gateway. For this side project, this currently costs way more running and invoking Lambda.

- Debugging can be a pain. Things like Sentry [3] make it better for runtime issues, but orchestration issues are still very trail and error.

- There can be overhead if your function goes "cold" (i.e. infrequent usage). Zappa lets you keep sites warm (additional cost), but a cold start adds a couple of seconds to the first-page load for that user. This applies more to low volume traffic sites.

Overall: It's definitely overkilled for a side project like this, but I could see the economics of scale kicking in for multiple or high volume apps.

[1]: https://blog.zappa.io/posts/zappa-introduces-seamless-asynch...

[2]: https://github.com/cameronmaske/s3env

[3]: https://getsentry.com/

Any comparisons to GCE and Azure's offerings for those who have used both?

After multiple side projects with Lambda (e.g. image processing services), we finally implemented it on larger scale. Initially we started out without any framework or tool to help, because there we pretty much non-existent at that time. We created our own tool, and used Swagger a lot for working with API gateway (because it is really bad to work with). Over time everything smoothened out and really worked nicely (except for API Gateway though). Nowadays we have everything in Terraform and Serverless templates, which really makes your life easier if you're going to build your complete infrastructure on top of AWS Lambda and other AWS APIs. There are still a bunch of quarks you have to work with, but at the end of the line: it works and you don't have to worry much about scaling.

I'm not allowed to give you any numbers; here's an old blogpost about Sketch Cloud: https://awkward.co/blog/building-sketch-cloud-without-server... (however, this isn't accurate anymore). For this use-case, concurrent executions for image uploads is a big deal (a regular Sketch document can easily exist out of 100 images). But basically the complete API runs on Lambda.

Running other languages on Lambda can be easily done and can be pretty fast, because you simply use node to spawn a process (Serverless has lots of examples of that).

Let me know if you have any specific questions :-)

Hope this helps.