Training or running AI models needs GPUs, and renting them isn't as simple as picking whatever cloud you already use — prices for the same NVIDIA chip can vary by 5x or more depending on where you rent it, and availability during busy periods is its own headache. Below are six real platforms people actually use to get GPU compute for AI work in 2026, from big-name enterprise clouds to a marketplace where anyone can rent out a spare card. Prices and specs are as published by each provider — always double-check current rates before committing, since GPU pricing moves fast.
1. CoreWeave — the enterprise-scale choice
CoreWeave is built specifically around NVIDIA GPUs for large-scale AI training and serving the biggest models, and it's trusted by major AI labs including OpenAI, Mistral AI, Google and IBM at production scale.
Pricing: on-demand H100s run about $6.16/GPU-hour, sold as 8-GPU nodes (~$49.24/hr total). Reserved capacity can cut that by up to 60%, spot instances save roughly 50%, and storage has zero egress fees.
Strengths: production-proven at the largest AI labs, a free Kubernetes control plane, and reserved pricing that meaningfully undercuts on-demand rates.
Limits: on-demand pricing is notably higher than RunPod's cheapest tiers, GPUs are sold in 8-GPU node bundles rather than individual cards (less flexible for small workloads), and the enterprise positioning means less self-serve simplicity than a platform like RunPod.
2. Lambda — research-first, scales to superclusters
Lambda scales from a single rented GPU up to superclusters exceeding 165,000 GPUs, with research-first origins dating back to 2012.
Pricing: standard instances run $0.79–$6.99/GPU-hour; dedicated B200 clusters run about $8.87–$9.86/GPU-hour for 16-256+ GPU commitments, with 1-Click Clusters available from two weeks and superclusters on multi-year terms.
Strengths: genuine scale (from one GPU to six-figure clusters), strong credibility with AI labs, fast provisioning in minutes, and SOC 2 Type II certification.
Limits: standard pricing doesn't clearly undercut RunPod's cheaper self-serve tiers, larger commitments require longer-term contracts, and the best value shows up at large-cluster scale rather than small, short-term rentals.
3. RunPod — pay-by-the-second, no contracts
RunPod is built for self-serve renting: pay by the second, no contracts, with fast serverless cold starts.
Pricing: Pods run $0.27–$7.39/hr per GPU, Serverless runs $0.58–$9.98/hr, and storage is $0.05–$0.14/GB/month — all pay-as-you-go by the millisecond.
Strengths: no contracts or minimum commitments, sub-200ms serverless cold starts via FlashBoot, and 30+ GPU types across 31 regions at prices generally cheaper than enterprise-focused competitors.
Limits: availability during high-demand periods can be less predictable than reserved capacity, support is lighter-touch than an enterprise contract with CoreWeave or Lambda, and the wide price range means real cost depends heavily on which GPU you pick.
4. Vast.ai — the marketplace option
Vast.ai works like Airbnb for GPUs: anyone with a spare gaming or server card can rent it out, so AI developers get compute at a fraction of what big clouds charge.
Pricing: three tiers — On-Demand (guaranteed uptime, billed by the second), Interruptible (50%+ cheaper but preemptible), and Reserved (up to 50% off over 1/3/6-month terms). 68+ GPU types across 40+ datacenters, with prices set by supply and demand.
Strengths: often 50%+ cheaper than traditional clouds thanks to the marketplace model, no minimum hours, and strong growth (310% in 2024) with SOC 2 Type I certification.
Limits: the cheapest Interruptible tier can get preempted by a higher bidder, hardware and network quality varies by individual host, and there are fewer uniform guarantees than a cloud with its own owned infrastructure.
5. Nebius — the European sovereign cloud
Nebius is a publicly-traded, European-built cloud specifically for AI workloads — training and inference — aimed at companies that want serious AI compute without depending on a US hyperscaler.
Pricing: H100 runs $2.15–$3.85/hr, H200 runs $2.45–$4.50/hr, B200/B300 runs $3.95–$8.50/hr (preemptible to on-demand), with CPU-only instances from $0.05–$0.10/hr. Reserved clusters get up to 35% off, with a $25 minimum.
Strengths: a credible European sovereign alternative to US hyperscalers, listed on Nasdaq for financial transparency, transparent published GPU pricing with no mandatory quote process, and established customers like Shopify and Revolut.
Limits: its corporate history (formerly part of Yandex) is worth knowing for vendor-risk evaluation, comparative TCO figures are self-reported and worth validating on your own workload, and prices generally run higher than a pure marketplace like Vast.ai.
6. SkyPilot — free, open-source, multi-cloud broker
SkyPilot isn't a GPU provider itself — it's an open-source tool that automatically finds and runs your training jobs on whichever cloud or cluster has the cheapest available GPUs at that moment.
Pricing: free and open source (Apache 2.0) — it runs entirely within your own cloud accounts, with no markup on compute.
Strengths: zero markup since there's no middleman, it automatically shops across 20+ clouds plus Kubernetes/Slurm clusters for the best price and availability, and it fails over automatically if your chosen provider runs out of capacity.
Limits: you still need actual cloud accounts with capacity to draw from, it offers no guaranteed dedicated capacity the way an enterprise contract with CoreWeave does, and enterprise features like SSO, RBAC and cost reporting require a paid managed layer on top of the free core.
GPU cloud platforms compared
| Platform | Model | Starting price | Best for |
|---|---|---|---|
| CoreWeave | Enterprise dedicated cloud | ~$6.16/GPU-hr (H100) | Large AI labs at production scale |
| Lambda | Research-first cloud, scales to superclusters | $0.79/GPU-hr | Labs and enterprises needing huge scale |
| RunPod | Self-serve pay-by-the-second | $0.27/GPU-hr | Developers wanting no contracts |
| Vast.ai | Peer-to-peer GPU marketplace | Market-priced, often cheapest | Budget-conscious training/inference |
| Nebius | European sovereign AI cloud | $2.15/GPU-hr (H100) | Teams wanting a non-US hyperscaler |
| SkyPilot | Free, open-source multi-cloud broker | Free (uses your own cloud accounts) | Teams already multi-cloud wanting lowest cost automatically |
Which one should you actually use?
If you need guaranteed capacity at serious scale and don't mind enterprise pricing, CoreWeave or Lambda are the safer bet. If you want to experiment or run smaller jobs without commitments, RunPod's pay-by-the-second model is hard to beat. If price is the only thing that matters and your workload can tolerate some preemption risk, Vast.ai's marketplace pricing usually wins. If data sovereignty or a non-US provider matters to your compliance team, Nebius is the clear pick. And if you're already spread across multiple clouds and just want the cheapest available GPU at any given moment without paying a markup, SkyPilot sits on top of whatever you already have and does the shopping for you.