If you're fine with using other individuals machines (meaning, you don't care about data privacy for the training set), using vast.ai is probably the cheapest way to do it today. But, quality of machines/network speed vary greatly as the machines are hosted by individuals around the world.

I've used it for a long while after the credits update and it's been cheaper for me for a few reasons than Lambdalabs, one reason being the spinup/spindown times and being able to switch to a very cheap instance for prototyping. Lambdalabs has a crazy spinup time (comparatively), something like 3-5 minutes or so D: I find them good for the more mega runs where I don't have to touch my instance for several hours and it's worth the notebook porting time/cost. :D

One of those usecases where raw cost doesn't always translate into money saved, at least in my personal experience. :D

It's also nice that you can more easily edit in browser in colab before launching an instance, so you can do development->debugging->training via a single webpage without it breaking the bank or having huge privacy concerns. I think a close second would be Jupyter on vscode w/ a lambda backend but latency + fragmented architecture can add an extra step of complexity (which does matter! D:)