There's no single "best" GPU cloud, because a solo developer fine-tuning a 7B-parameter model and a lab pretraining a frontier model from scratch need almost nothing in common from a provider. The right pick depends on how your job is shaped — not on which provider has the lowest sticker price per hour.
The Real Split: Hyperscalers vs. Neoclouds
AWS, Google Cloud, and Azure rent H100/Blackwell-class GPUs alongside their entire existing platform — compliance certifications, managed data pipelines, enterprise support contracts, the works. A newer category of "neocloud" providers (RunPod, Lambda Labs, CoreWeave, Vast.ai, and others) specializes purely in GPU rental, with far less surrounding infrastructure but often faster self-serve access and lower prices, since they're not carrying the same platform overhead.
What Actually Matters: Single-Node vs. Multi-Node
If a job fits on one node — even a large one, like 8×H100 — network interconnect between GPUs barely matters, since most communication stays inside the node. Once a training run spans multiple nodes and needs to synchronize gradients between them constantly, interconnect quality (InfiniBand-class fabric, which CoreWeave has invested in heavily) becomes the actual bottleneck, not the advertised $/hr rate.
Single-node fine-tuning is bandwidth-light. Multi-node distributed training is bandwidth-bound. A cheaper GPU with a slow interconnect can end up costing more per successful run than a pricier one, once you count the hours lost to network-stalled utilization.
Spot vs. On-Demand vs. Reserved
Spot pricing is the cheapest option but can be preempted mid-run — it's a good fit for anything that checkpoints and resumes cleanly. On-demand costs more but guarantees the node stays yours for the session. Reserved or committed-use pricing is cheapest per hour of all, in exchange for committing to usage in advance — a real option once your training workload is predictable enough to plan around. Neoclouds tend to have the simplest self-serve spot markets; hyperscaler reserved-instance programs usually require longer commitments in exchange for broader platform integration.
Onboarding Friction Is a Real Cost, Not Just an Annoyance
A self-serve neocloud can get a GPU node running within minutes of signing up with a credit card — but support is often community-driven or ticket-based rather than a dedicated account team. Hyperscalers offer real enterprise support, but provisioning a large GPU quota can take a formal request and days of waiting. If you need capacity today, that difference matters as much as the hourly rate.
A Practical Way to Choose
If the job fits on one node and cost matters most, a neocloud's spot market is usually the right starting point. If the job spans multiple nodes and throughput matters more than the sticker price, look specifically for a provider advertising high-bandwidth interconnect, not just a low $/hr figure. If there's a real compliance or data-residency requirement, that alone usually settles it in favor of a hyperscaler, premium included.
Is a neocloud like RunPod or Lambda Labs actually cheaper than AWS or GCP for GPU training?
Generally yes for on-demand or spot single-node jobs, since neoclouds don't carry the same enterprise-support and compliance overhead baked into hyperscaler pricing. The gap narrows once a hyperscaler's reserved or committed-use discounts come into play.
Do I need InfiniBand-class networking for fine-tuning a single model?
Almost never, if the fine-tune fits on one node's GPUs. It starts to matter once a training job is sharded across multiple nodes and needs fast gradient synchronization between them.
What's the actual risk of using spot instances for training?
Preemption mid-run. It's manageable with a checkpoint-and-resume setup, but a training script that can't checkpoint cleanly will lose progress whenever the instance gets reclaimed.