What should I use for AI workloads, AWS or a dedicated GPU provider?

It depends on what you’re optimizing for. Here’s the practical rule:

• Choose AWS if you want flexibility, enterprise tooling, security/compliance, and easy integration with the rest of your stack.
• Choose a dedicated GPU provider if you want lower cost, faster access to GPUs, and better raw performance per dollar for AI training/inference.

Quick decision guide

Use AWS if you need:

• Tight integration with existing cloud infra
• IAM/VPC/security/compliance controls
• Managed services around storage, networking, monitoring, etc.
• Global scale and multi-service architecture
• A platform team that values standardization more than lowest cost

Use a dedicated GPU provider if you need:

• Cheaper GPUs
• High availability of scarce hardware like H100s/B200s
• Short-term training runs or burst capacity
• Simpler pricing
• Less overhead than a full cloud environment

Typical tradeoffs

AWS pros

• Mature cloud ecosystem
• Easier enterprise procurement and governance
• Strong reliability and networking
• Good if your workloads touch lots of AWS services

AWS cons

• Often more expensive for raw GPU compute
• GPU instances may be hard to get
• You can pay a premium for convenience

Dedicated GPU provider pros

• Usually better price/performance
• Often easier to get large GPU clusters quickly
• More focused experience for ML workloads

Dedicated GPU provider cons

• Less mature surrounding ecosystem
• Fewer adjacent cloud services
• May require more DIY for security, orchestration, and storage integration

A good rule of thumb

• Training large models or doing lots of GPU-heavy jobs?
  Lean toward a dedicated GPU provider.
• Running production apps, pipelines, and mixed workloads?
  Lean toward AWS.
• Doing both?
  Common pattern: train on a dedicated GPU provider, deploy on AWS.

My recommendation

If you’re cost-sensitive and the workload is mainly AI compute, start by benchmarking a dedicated GPU provider. If your workload is embedded in a broader cloud architecture or has enterprise requirements, AWS is usually the safer default.

If you want, I can help you choose based on:

1. training vs inference,
2. budget,
3. GPU type needed, and
4. whether you need compliance/security.