Cloud GPU Provider Comparison 2026: AWS vs GCP vs Azure vs Lambda vs CoreWeave vs RunPod vs Vast.ai

Summary Comparison Table

All pricing pulled live from provider APIs every 30 minutes. Price ranges reflect current spot and on-demand availability.

Amazon Web Services (AWS)

AWS is the default choice for enterprises — and the most expensive for pure GPU throughput. GPU instances run on EC2 under the p5 (H100), p4d/p4de (A100), and p3 (V100) families. SageMaker wraps these with managed ML infrastructure at an additional premium.

AWS GPU spot instances (EC2 Spot) offer meaningful discounts but require interruption handling. For production inference, most teams end up on On-Demand or Savings Plans — which narrows the price advantage significantly.

Google Cloud Platform (GCP)

GCP is the strongest hyperscaler for ML workloads due to its native integration with Vertex AI, TensorFlow ecosystem, and proprietary TPU v5 access. For GPU specifically, GCP offers A100s and H100s via Compute Engine, and preemptible (spot) instances at meaningful discounts.

GCP's managed ML pipeline (Vertex AI Pipelines, Model Registry, Experiments) makes it the hyperscaler choice for teams standardizing their MLOps stack. Pure compute price-per-GPU is competitive with AWS but still trails specialized GPU clouds.

Microsoft Azure

Azure's GPU offering targets the enterprise market with NDv4 (A100) and NDv5 (H100) series. Azure Machine Learning (AML) provides end-to-end MLOps tooling, and Azure OpenAI Service gives enterprise access to GPT-4 models — making Azure the default for Microsoft-stack organizations.

Azure Spot VMs offer GPU discounts, though availability in premium GPU SKUs is often constrained. For organizations already on M365, Azure AD, and Microsoft security stack, Azure reduces operational friction even if raw GPU pricing isn't the most competitive.

Lambda Cloud

Lambda Cloud is purpose-built for ML teams that want GPU access with zero overhead. No spot market, no complex pricing tiers — just flat on-demand rates that are substantially cheaper than AWS/GCP/Azure for equivalent hardware.

Lambda's H100 and A100 pricing regularly undercuts the hyperscalers by 40-60% on on-demand rates. No egress fees, no storage markups, no support tier upsell. This simplicity attracts researchers and ML startups that find hyperscaler complexity a tax on their time.

CoreWeave

CoreWeave is the GPU cloud designed by ML engineers for ML engineers. NVIDIA-partnered, Kubernetes-native, and built exclusively around GPU workloads since 2019. CoreWeave offers the widest selection of current-generation NVIDIA hardware — H100, H200, A100, A40, RTX 4090 — with both spot and reserved options.

At scale, CoreWeave competes directly with hyperscalers on raw GPU throughput while offering better performance-per-dollar. Their interconnect and NVLink topology is optimized for large model training. Best-in-class for companies running inference at serious scale.

RunPod

RunPod operates a two-tier model: Secure Cloud (their own datacenter infrastructure) and Community Cloud (idle GPUs from independent hosts). Community Cloud offers the lowest spot prices in the market but without SLA guarantees. Secure Cloud provides more reliability at a modest premium.

RunPod's developer experience is excellent — one-click container deployments, serverless GPU endpoints, and a template library covering popular models (Stable Diffusion, LLaMA, Whisper). The tradeoff: community pods can disappear, and uptime SLAs aren't contractual.

Vast.ai

Vast.ai is a decentralized GPU marketplace where independent hosts rent out idle compute. This model pushes prices to near-hardware cost — you'll routinely find RTX 4090 and A100 instances at prices that don't exist anywhere else. The tradeoff is that you're renting from individuals, not datacenters.

Vast.ai's bidding system lets you set your price target and receive instances when matching supply appears. For overnight batch jobs, exploratory training runs, and cost-maximizing inference, Vast.ai is in a different price tier from every other option.

How to Choose the Right Provider

The right provider depends on your workload type, reliability requirements, and existing infrastructure. Here's the decision tree most ML teams end up using:

Whichever provider you choose, monitor actual spot prices before you commit. Provider pricing changes frequently, and the difference between the cheapest and most expensive option for the same GPU can be 5-10× on any given day. RoofRun tracks all 7 providers every 30 minutes so you always have current data.