AWS vs Specialized GPU Cloud for H100 Inference

Short Answer

AWS is not automatically wrong for H100 inference. It is often the rational choice when the workload is already inside AWS, depends on surrounding managed services, or needs predictable enterprise controls.

But if the inference workload is portable and the main constraint is GPU-hour cost, specialized GPU clouds deserve a first quote before AWS becomes the default.

The Decision Variable

The question is not "which provider has the cheapest H100?"

The better question is:

how many useful GPU-hours will this workload consume, and what has to move around those GPU-hours?

For inference, the hidden costs are usually:

• idle GPU time between traffic spikes
• model storage and image pulls
• egress from user traffic or downstream systems
• queue time and cold starts
• reliability work if the provider is less managed
• engineering time to operate deployment, monitoring, rollback, and scaling

Rough Comparison

Directional only. Check current provider pricing before buying capacity.

When AWS Is Probably Still Right

AWS is likely the better first placement when:

1. The data already lives in S3, DynamoDB, RDS, Redshift, or another AWS service.
2. The inference endpoint must sit close to an existing AWS application.
3. Security review favors AWS over smaller providers.
4. You need committed capacity or a procurement path your company already understands.
5. The GPU bill is meaningful, but not large enough to justify migration and operations work.

The mistake is not choosing AWS. The mistake is choosing AWS because nobody priced the workload any other way.

When Specialized GPU Cloud Is Worth Pricing

Specialized GPU cloud becomes interesting when:

• the workload is mostly self-contained
• model artifacts are easy to move
• latency requirements are flexible
• GPU utilization is high enough that hourly spread matters
• the team can tolerate some provider evaluation work
• the same model can run without deep AWS-specific dependencies

If the workload uses hundreds or thousands of GPU-hours per month, even a small hourly delta can become material.

What To Collect Before Deciding

Collect these before comparing quotes:

1. average tokens or requests per second
2. peak concurrency
3. model size and GPU memory requirement
4. expected useful GPU-hours per month
5. idle percentage
6. model artifact size
7. data egress and user geography
8. acceptable cold start time
9. rollback and monitoring needs
10. current AWS bill line items if already deployed

Decision Rule

If the workload is tightly coupled to AWS data or managed services, start with AWS and optimize around utilization. If the workload is portable and GPU-hours dominate cost, price at least two specialized GPU clouds before committing.

Use RunPlacement

Use the RunPlacement quiz to pressure-test whether the workload is really an AWS workload or a portable GPU workload.

How To Use This Page

Treat this page as a placement filter, not a provider ranking. The goal is to narrow the next quote or benchmark you should run.

Use it in this order:

1. Identify whether the workload is experimental, bursty, steady, or production-critical.
2. Estimate useful compute time rather than provisioned time.
3. Write down the data movement and storage around the compute.
4. Decide how much operational variance the team can tolerate.
5. Compare providers only after the workload shape is clear.

This matters because two teams can look at the same pricing page and need opposite answers. A research team running checkpointed experiments can accept interruptions and provider variance. A production inference team with strict latency and support requirements may rationally pay more for the same visible GPU.

What Would Change The Answer

The recommendation changes quickly when one of these inputs changes:

• the model no longer fits on the cheaper GPU
• latency or throughput becomes the business constraint
• training time affects a launch date or customer commitment
• data already lives inside one cloud and is expensive to move
• compliance or procurement rules exclude smaller providers
• the workload becomes steady enough to justify committed capacity
• the team cannot absorb extra monitoring, restarts, or provider debugging

This is why RunPlacement asks about priority, GPU need, data movement, and ops tolerance. The placement decision is usually hiding in those tradeoffs, not in the headline hourly price.

Evidence And Sources

This draft uses public pricing or provider documentation plus real-world confusion signals where available:

• https://aws.amazon.com/ec2/capacityblocks/pricing/
• https://aws.amazon.com/about-aws/whats-new/2025/06/pricing-usage-model-ec2-instances-nvidia-gpus/
• https://lambda.ai/pricing
• https://www.runpod.io/pricing/

Target queries for this page:

AWS vs GPU cloud for H100 inference, where to run H100 inference, specialized GPU cloud vs AWS, H100 inference cloud cost

Assumptions

• The workload can technically run outside AWS.
• The buyer can compare at least two providers before committing.
• The workload has measurable GPU-hours and data movement.

FAQs

Q: Is AWS always more expensive for H100 inference? A: No. AWS may be cheaper in total if it avoids data movement, compliance work, or operational complexity. Q: Are specialized GPU clouds always production-ready? A: No. You still need to check availability, support, networking, monitoring, and incident handling. Q: What is the first number to estimate? A: Useful GPU-hours per month, not just the listed GPU hourly rate.

Final Placement Rule

If data gravity and managed services dominate, start with AWS. If portable GPU-hours dominate, price specialized GPU clouds first.

Pressure-Test It

Before you buy capacity or migrate the workload, run the RunPlacement quiz with the actual workload shape. A rough answer with the right missing variables is more useful than a precise-looking quote for the wrong comparison.