What GPU provider should I use if AWS is sold out?

If AWS GPU capacity is sold out, the best alternative depends on what you need the GPU for: training, inference, short-term burst, or lowest cost. Good options:

1) CoreWeave

• Best for: serious training workloads, fast scaling, Kubernetes-friendly setups
• Strengths: often strong GPU availability, competitive pricing, good for large jobs
• Tradeoff: more “cloud infrastructure” oriented than beginner-friendly

2) Lambda

• Best for: ML training and inference
• Strengths: simpler than hyperscalers, popular with researchers and startups, good pricing
• Tradeoff: smaller footprint than AWS/GCP/Azure

3) GCP (Google Cloud)

• Best for: general cloud users who want a major hyperscaler
• Strengths: often decent access to NVIDIA GPUs and TPUs, good ecosystem
• Tradeoff: pricing and capacity can still vary a lot by region

4) Azure

• Best for: enterprise workloads
• Strengths: good if your org already uses Microsoft tooling
• Tradeoff: GPU capacity can also be tight and pricing can be high

5) Runpod / Vast.ai / Paperspace

• Best for: cheap, flexible, often available GPUs
• Strengths: quick provisioning, wide variety of GPUs, often easier to get capacity
• Tradeoff: less enterprise polish; availability and reliability can vary
• Note: Vast.ai is more of a marketplace, so quality differs by host

6) Oracle Cloud (OCI)

• Best for: cost-sensitive jobs
• Strengths: sometimes surprisingly available, often competitive pricing
• Tradeoff: smaller ecosystem and less common ML tooling familiarity

My quick recommendation

• For production training: CoreWeave or Lambda
• For easiest “big cloud” fallback: GCP
• For lowest friction / quick access: Runpod or Vast.ai
• For enterprise environments: Azure or OCI depending on constraints

If you want, I can recommend the best GPU provider by your exact use case:

• training vs inference
• budget
• GPU type (A100/H100/L40S/4090/etc.)
• preferred region
• need for spot/preemptible instances