Startup founder here — curious how you choose your cloud GPU provider?

In my experience working with ML-heavy workloads, most teams end up balancing a few key factors rather than optimizing for just one.

Price is obviously important, but it’s usually the price-to-performance that matters more. For example, platforms like Lambda Labs are known for stable infrastructure, while marketplaces like Vast.ai can sometimes offer very low prices depending on supply.

Availability and queue times are often the real bottleneck. Some services look great on paper but GPUs are frequently unavailable when you actually need them. That’s why many teams test multiple providers such as RunPod or smaller platforms like GPUhub to see which one consistently has capacity.

GPU type and VRAM also matters a lot, especially for LLM fine-tuning or large inference workloads. Access to cards with large VRAM (A100 / H100 / RTX-class with high memory) can significantly simplify deployments.

Finally, deployment experience is underrated. Some teams prefer fully managed MLOps stacks, but many ML engineers just want simple SSH/Docker environments where they can spin up GPUs quickly and run their own pipelines.

In practice, many startups end up using multiple providers depending on the workload (training vs inference vs experimentation).

Curious what others here prioritize as well.