The serverless GPU category exists because of a genuine mismatch. Renting a GPU instance by the hour is the wrong shape for inference traffic that arrives in bursts, sits idle overnight and spikes unpredictably, and the alternative of managing your own autoscaling GPU fleet on Kubernetes is a platform engineering project most teams cannot justify. Cerebrium sits in that gap alongside a crowded field, and its documentation is example-driven in a way that suits the problem: you arrive wanting to deploy something specific, and the docs give you a path to it rather than a tour of concepts. The quickstart does what it should.
Install the CLI, authenticate, initialise a project, configure, deploy, and you have a GPU endpoint. The starting paths cover an OpenAI-compatible LLM deployment using vLLM, image generation with SDXL, a Gradio app, and voice AI with Twilio and Pipecat. That last one is the most interesting piece of documentation here and the reason I would point people at it even if they deploy elsewhere. Real-time voice is a genuinely hard engineering problem that the general inference literature mostly skips.
You are chaining speech recognition, a language model and speech synthesis inside a latency budget where anything past a few hundred milliseconds makes the conversation feel broken, and every architectural decision, where the model runs, how audio is streamed, how turn detection works, what happens on interruption, is constrained by that budget. Documentation that walks through an actual working pipeline for this is more useful than a dozen generic inference guides, and the concepts transfer to any platform. Cold starts are the central technical claim and the one you cannot evaluate from documentation. Every serverless GPU provider advertises low cold starts because the whole category lives or dies on them: a platform that takes ninety seconds to spin up a container with a large model loaded is not serverless in any sense that matters for user-facing inference.
Cerebrium claims fast cold starts and burst scaling. The docs explain configuration that affects them. Nothing there lets you verify the numbers, and I would treat published cold start figures from any vendor in this space as a starting point for your own measurement rather than a fact. Test with your actual model at your actual size, because the performance profile of a seven billion parameter model and a seventy billion parameter one are not the same problem.
What the documentation does not teach at all is the economics, and this is the decision most teams get wrong. Per-second billing is excellent for genuinely bursty traffic and terrible for sustained load, where a reserved instance is dramatically cheaper. The crossover point depends on your utilisation pattern, your model size and how much you value not operating infrastructure, and working it out requires arithmetic nobody publishes because the answer is sometimes do not use us. This is the same structural silence I have written about repeatedly: a vendor billing by consumption has no incentive to teach you when consumption billing is the wrong model.
The portability cost is the other thing worth naming. You are deploying against a proprietary surface with its own configuration format, CLI and scaling semantics. Containerisation limits the damage compared with a fully managed abstraction, and a migration to a different provider still means rewriting deployment configuration and rethinking scaling behaviour. That is not a reason to avoid the category and it is a reason to keep your actual application code cleanly separated from the platform glue, which the documentation does not advise you to do.
Vendor longevity deserves a thought. This is a crowded market with well-funded competitors and a set of capabilities that the large clouds are steadily absorbing. The product is good and the category has consolidation ahead of it. Build accordingly.
Three point six. Practical, example-led documentation with an unusually good real-time voice guide, teaching a specific platform competently while leaving the cost modelling question, which is the one that actually decides whether you should use it, entirely unaddressed.