Which GPU cloud provider are you actually using for inference?

Community Calls
1

Hey everyone,

I’ve been looking at providers like RunPod, Vast.ai, Lambda Labs, and a few others, and every time I need GPU capacity I end up spending way too much time comparing them. Prices change, availability changes, and it’s hard to know which providers are actually reliable in practice.

I’m working on a tool that recommends a provider based on your specific use case (model, workload, region, priorities, etc.) instead of just showing a list of prices.

Before I invest more time into it, I’d love to hear how people are handling this today: Which provider are you currently using, and what made you choose it? Do you regularly switch providers, or mostly stick with one? What’s the most frustrating part of choosing a GPU cloud provider?

Any real-world experiences would be super helpful. Thanks!

3

I had the same problem, so I ended up building something around it.

With Orchard Compute, I just connect it through MCP and describe what I need in one sentence. It can provision the compute and set up the training environment without me manually checking different GPU providers every time.

Still early, but that’s the workflow I’m trying to get to.

Would love to hear your feedback! orchardagentic.cloud

4

I always change the providers according to the price. Because my application may not care the models. But it is productivity.

6

Honestly, I haven’t stuck with just one provider for inference it really comes down to the workload, the budget, and where I actually need to deploy. Over the past year I’ve used or at least tested RunPod, Lambda, Vast.ai, TensorDock, Crusoe Cloud, CoreWeave, Nebius, and more recently NeevCloud, all for different projects.Each one brings something different to the table. RunPod’s great when you just want to get up and running fast. Vast ai can be really cost-effective if you’re flexible about specs. Lambda has been dependable for the longer-running stuff. CoreWeave feels built for production-scale deployments. And NeevCloud has actually surprised me. It’s been a smooth experience for a few inference workloads I tried recently. These days, I spend way less time comparing GPU models and a lot more time looking at things like uptime, instance availability, networking quality, support responsiveness, and how predictable the billing actually is. At the end of the day, a provider that saves me hours of debugging is worth a lot more to me than one that’s slightly cheaper per GPU hour.