The gap this fills is one that anyone who has tried to run a model on cloud hardware knows intimately. You have a Python script. It needs a GPU. Between those two facts sits an enormous amount of work that has nothing to do with your actual problem.
Writing a Dockerfile. Getting CUDA versions to agree. Pushing images to a registry. Provisioning instances or configuring a cluster.
Setting up autoscaling so you are not paying for an idle card overnight. Working out storage. Most of a week, for a script that runs in four minutes. The pitch here is that you add a decorator to your function and it runs on a GPU in the cloud, and the remarkable thing is that this is broadly true.
You describe your container image in Python rather than in a Dockerfile. You attach hardware requirements as a function argument. You deploy from the command line and get a URL. There is no cluster, no registry, no YAML, and the documentation walks you through it without ceremony.
Within an hour of starting you have something running on hardware you do not own, and that speed is the whole product. Cold start performance is the technical achievement that makes it more than a convenience. Serverless GPU has been promised repeatedly and has usually meant waiting thirty seconds for a container to come up, which kills every interactive use case. The startup times here are short enough that request-driven inference is genuinely practical, and the documentation explains the mechanisms well enough that you can reason about when they will and will not help you.
The examples collection is one of the better things in this catalogue. It covers real workloads, fine-tuning, batch inference, web endpoints serving models, audio and image pipelines, and the examples are maintained and actually run when you copy them. That last part sounds like a low bar and almost nobody clears it. Reading through them is a genuinely good way to learn the platform, and also a decent way to see how a competent engineer structures this kind of work.
Per-second billing deserves specific mention because it changes behaviour. When spinning up a large GPU for six minutes costs pennies, you try things. You benchmark three model sizes instead of guessing. You run the experiment rather than reasoning about whether it is worth the setup.
That shift from careful rationing to casual experimentation is worth more than the raw price difference, and it is the strongest argument for this style of platform. Now the honest concerns. The lock-in is complete and the documentation is quiet about it. Your infrastructure is not configuration that could be pointed at a different provider, it is decorators and objects from one vendor's library woven through your application code.
Moving off means rewriting the structure of your program, not migrating a configuration file. That is the deal, the convenience comes precisely from the coupling, and a technical decision-maker should be making that trade deliberately rather than discovering it in year two. The documentation never frames it as a trade at all. Cost surprises are the practical risk that follows from the ergonomics being this good.
Requesting eight high-end GPUs is one argument. There is nothing in the workflow that makes you pause and consider what that costs per hour, and the billing documentation is separate from the pages where you make the decision. Teams should set spending limits on day one, and the documentation should say so much earlier and much louder than it does. Debugging is inherently harder when your code runs somewhere else, and the material here is adequate rather than excellent.
There are logs, there is a shell into the environment, there are ways to run locally. When something behaves differently remotely than locally, which is common with hardware-dependent code, you are more on your own than the rest of the documentation prepares you for. The gravitational pull towards platform primitives is worth noticing. Volumes, dictionaries, queues, scheduled functions, all convenient, all specific to here.
Each one you adopt deepens the coupling. A team that wanted portability could use the compute and keep state in their own systems, and the documentation naturally does not suggest that path. My four point zero is for genuinely solving the problem it set out to solve, with an ergonomics-first design that removes real work, fast enough cold starts to make the model viable, examples that run, and pricing that encourages experimentation. Marked down for lock-in that is never presented as a decision, for making expensive choices frictionless, and for debugging support that lags the rest of the experience.
The best in its category. Choose it with your eyes open.