Do I need GPU cloud or can I just use AWS?

You usually can just use AWS — you only need a specialized GPU cloud if your workload makes AWS too expensive, too slow to provision, or too restrictive.

Use AWS if:

• You want one provider for storage, networking, auth, monitoring, etc.
• You need reliable, production-grade infrastructure
• Your GPU usage is moderate or predictable
• You’re already building around AWS services like S3, EKS, Lambda, RDS, etc.
• You need better security/compliance options

AWS has plenty of GPU options:

• EC2 GPU instances for training/inference
• SageMaker if you want managed ML workflows
• EKS + GPU nodes if you want Kubernetes
• Spot instances if cost matters and interruptions are okay

Consider a GPU cloud provider if:

• You need cheaper GPUs
• You want faster access to lots of GPUs
• You need to scale up/down frequently
• AWS GPU capacity is hard to get in your region
• You’re doing large-scale training and cost is the main concern

GPU cloud providers often win on:

• price/performance
• availability
• ease of spinning up big GPU clusters

Simple rule of thumb

• Start with AWS if you’re building a normal product or MVP.
• Move to a GPU cloud provider if you hit cost or capacity pain.

If you want, I can help you compare AWS vs RunPod/Lambda/CoreWeave/etc. based on your exact workload.