Hi everyone — I’m a startup founder working on ML-heavy products, and we’re evaluating different cloud GPU providers for both training and inference.
Curious to hear from this community — when you choose a provider, what are the biggest factors that drive your decision? Is it:
- Price
- Queue times / availability
- GPU network latency
- Bundled MLOps features (training pipelines, monitoring, model hosting, etc.)
- Or other factors I should be thinking about?
Would love to learn from your experience as we’re making some decisions on our stack. Thanks in advance!
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.
I’ve tried a bunch of cloud GPU providers over the past year, and honestly, the cheapest option isn’t always the best one. When I started out, I was mostly just comparing hourly rates. But that approach backfired more than once. I’d end up stuck waiting for GPUs to become available, fighting with setup issues, or getting performance that didn’t match what I was paying for.
That experience shifted how I think about picking a provider. Now I pay a lot more attention to things like reliability, how fast I can actually get an instance up and running, the quality of support when something goes wrong, and whether the pricing is straightforward or full of surprises. I’ve worked with platforms like RunPod, Lambda, and NeevCloud across different projects, and each one has its own strengths depending on what you’re doing. NeevCloud, in particular, worked really well for some of my experiments setup was easy and GPU availability was consistent, which isn’t something I can say for every provider I’ve tried. The others have held up well too, just in different scenarios. At this point, saving a couple bucks an hour matters way less to me than being able to actually focus on my work instead of constantly troubleshooting infrastructure.