GPU work costs less now
If most of your bill is GPU time, a neocloud (a specialist cloud provider) is usually cheaper than AWS, Azure, or Google Cloud for the same compute. You get fewer managed services and run more of the setup yourself, so a neocloud suits some workloads and not others. So what’s a neocloud, what does it cost, and when is it worth a look?
The neocloud
A neocloud is a cloud provider built around one thing: renting out GPUs for heavy compute. CoreWeave, Lambda, Nscale, Nebius, Crusoe, and Civo are some of the names you’ll come across if you go looking. They don’t try to match the hundreds of services the big three offer, since that’s not their business model. They focus on GPU-as-a-service and price it keenly, which is why teams running big training or inference jobs move their work to them.
Why they cost less
The lower price comes down to two things: less overhead, because they aren’t running a giant catalogue of managed services alongside the GPUs, and hard competition on the one number a GPU customer cares about, the price per GPU-hour. There’s real demand behind it. Gartner expects neoclouds to take about 20 per cent of a 267-billion-dollar AI cloud market by 2030, and the GPU-as-a-service revenue is forecast to grow from roughly 42 billion dollars in 2025 to more than 250 billion by 2030. Competition at that scale keeps prices down.
The trade-offs
You give things up for that price. There’s no deep catalogue of managed databases and queues, so anything beyond raw compute you tend to run yourself. Capacity can be tight, and the newest GPUs sell out, so you can’t always get what you want the moment you want it. Support is lighter than an enterprise account on the big three. And your other services, your database and your storage, will probably still sit on a hyperscaler, so you need to weigh the cost and latency of moving data between them.
When it’s worth a look
A neocloud is worth pricing up when GPU time is a big share of your bill and the workload is portable enough to move; a container you can run anywhere helps here. If you lean heavily on a hyperscaler’s managed services, or you want everything in one place with one bill and one support line, staying put is the reasonable call. For a lot of AI-heavy teams, the sensible setup is a split: the GPU work on a neocloud, and the rest where it already runs.
