The Great GPU Migration of 2024-2025

Something interesting is happening in the AI industry: startups are quietly migrating away from AWS, GCP, and Azure for their GPU workloads. And the numbers are staggering.

70%
Average cost savings
3x
Better GPU availability
90%
Faster deployment

Why AWS Made Sense (Before)

For years, AWS was the default choice for ML infrastructure:

But as AI workloads grew, cracks appeared in this strategy.

The 5 Reasons Startups Are Leaving

1. The Price Gap Became Unsustainable

AWS GPU pricing hasn't kept pace with the market (see the example scenario below).

When you're burning through venture capital, cutting the GPU bill by about two-thirds isn't a nice-to-have—it's survival.

2. GPU Availability is Still Terrible

Despite the AI boom, getting H100s on AWS remains a challenge:

Specialized providers built their entire business around GPU availability. It's not an afterthought.

3. Simpler Is Better

AWS gives you 200 services. For ML training, you need:

  1. A GPU
  2. SSH access
  3. Fast storage

That's it. The complexity of VPCs, IAM roles, and service configurations isn't adding value—it's adding overhead.

💡 Time-to-First-Training

AWS: 2-3 hours (quotas, VPC setup, security groups)
GPUBrazil: 5 minutes (sign up, deploy, SSH)

4. Startup Credits Run Out

AWS Activate gives startups $100k in credits. Sounds great until:

Many startups discover the true cost only after credits expire, creating a painful transition.

5. The Ecosystem Advantage Doesn't Apply

AWS's moat is ecosystem integration. But for ML workloads:

The ML stack is inherently portable. You're not locked into AWS.

What Startups Are Switching To

Specialized GPU Clouds

The Hybrid Approach

Smart teams use multiple providers:

Example Scenario: AI Startup Migration

Example scenario (hypothetical): a Series A computer vision startup with this setup.

Before (AWS):

After (GPUBrazil + S3):

GPUBrazil prices checked on Oct 28, 2026; they vary with availability.

Annual savings: $262,296

That's engineer salaries. That's extended runway. That's survival.

How to Migrate

The migration is simpler than you think:

  1. Keep S3 for data: It's cheap and works with any GPU provider
  2. Containerize workloads: Docker makes migration trivial
  3. Test parallel: Run one training job on GPUBrazil while AWS continues
  4. Gradual shift: Move training first, then inference

Most teams complete migration in 1-2 weeks.

Join the Migration

Run one training job on GPUBrazil next to your AWS setup and compare the bills.

Create free account →

The Future: Best-of-Breed Infrastructure

The days of single-cloud architectures are ending. The new model:

This approach gives you the best price-performance at each layer rather than accepting whatever one vendor offers.

Conclusion

The shift from hyperscalers to specialized GPU clouds is accelerating. It's driven by simple economics:

If you're still running GPU workloads on AWS at full price, you're leaving money on the table—money that could be extending your runway or hiring more engineers.

Try GPUBrazil and see what the fuss is about. Your CFO will thank you.