Choosing the Right GPU Cloud in 2025
With AI demand exploding, dozens of GPU cloud providers have emerged. Choosing the right one can save you thousands of dollars and hours of frustration. This guide compares the top options across price, performance, availability, and features.
Quick Comparison: Price per GPU-Hour
| Provider | H100 80GB | A100 80GB | L40S | RTX 4090 |
|---|---|---|---|---|
| AWS | $12.29 | $4.10 | $1.83 | N/A |
| Google Cloud | $10.98 | $3.93 | $1.75 | N/A |
| Azure | $11.82 | $3.67 | $1.70 | N/A |
| Lambda Labs | $2.99 | $1.29 | N/A | $0.75 |
| RunPod | $3.99 | $1.64 | $0.94 | $0.44 |
| Vast.ai | Varies | $1.20-2.00 | $0.60-1.00 | $0.30-0.50 |
| GPUBrazil | $2.80 | $1.60 | $0.90 | $0.55 |
💡 Key Insight
Hyperscalers (AWS, GCP, Azure) charge 3-4x more than specialized GPU clouds. Unless you need specific integrations, you're overpaying.
Provider Deep Dive
🟠 Amazon Web Services (AWS)
Best for: Enterprise teams with AWS commitments, need for tight S3/Lambda integration
Pros: Vast ecosystem, reliable, global availability, spot instances
Cons: Very expensive, complex pricing, GPU availability issues
H100 availability: Often limited, requires Reserved Instances for guaranteed access
🔵 Google Cloud Platform (GCP)
Best for: TPU users, TensorFlow-heavy teams, Vertex AI users
Pros: TPU access, good ML tooling, competitive A100 spot prices
Cons: Expensive on-demand, limited H100 availability, complex quotas
🟣 Microsoft Azure
Best for: Microsoft ecosystem users, OpenAI API access needs
Pros: Azure ML integration, OpenAI partnership, enterprise features
Cons: Expensive, limited GPU availability, slow provisioning
🟡 Lambda Labs
Best for: Researchers, startups, ML teams wanting simplicity
Pros: Good prices, simple interface, fast spin-up, ML-focused
Cons: Limited H100 availability, US-only, fewer regions
🔴 RunPod
Best for: Inference workloads, Stable Diffusion users, hobbyists
Pros: Serverless option, templates, community pods
Cons: Variable quality (community hardware), less professional
⚪ Vast.ai
Best for: Budget-conscious users, non-critical workloads
Pros: Lowest prices, marketplace model, variety of GPUs
Cons: Unreliable hosts, variable performance, minimal support
🟢 GPUBrazil
Best for: ML teams, startups, researchers wanting best price-performance
Pros: Lowest H100 prices, instant deployment, no hidden fees, excellent support, FLEX & PREMIUM tiers
Cons: Newer provider, fewer regions (expanding)
Feature Comparison
| Feature | AWS | GCP | Lambda | GPUBrazil |
|---|---|---|---|---|
| Instant deployment | ❌ (minutes) | ❌ (minutes) | ✅ | ✅ |
| No commitment | ❌ | ❌ | ✅ | ✅ |
| SSH access | ✅ | ✅ | ✅ | ✅ |
| API access | ✅ | ✅ | ✅ | ✅ |
| Pre-built ML images | ✅ | ✅ | ✅ | ✅ |
| Multi-GPU (8x H100) | ✅ | ✅ | ✅ | ✅ |
| Data transfer fees | $0.09/GB | $0.12/GB | Free | Free |
| Crypto payment | ❌ | ❌ | ❌ | ✅ |
When to Use Each Provider
Use AWS/GCP/Azure if:
- Your company has existing enterprise agreements
- You need specific compliance (HIPAA, FedRAMP, etc.)
- Tight integration with cloud-native services is critical
- Budget is not a primary concern
Use Lambda Labs if:
- You want a simple, no-frills GPU cloud
- US-based servers are sufficient
- You need persistent workstations
Use GPUBrazil if:
- Cost matters (best price-performance ratio)
- You want instant deployment without quotas
- You need H100s at the lowest price
- You want flexibility (FLEX vs PREMIUM tiers)
- You prefer pay-as-you-go with no commitments
Real Cost Scenario: Training a 7B Model
Let's calculate the cost of training a 7B parameter model for 50 hours:
| Provider | GPU | Hourly Rate | 50hr Cost |
|---|---|---|---|
| AWS p5 | 8x H100 | $98.32 | $4,916 |
| GCP a3-highgpu | 8x H100 | $87.84 | $4,392 |
| Lambda Labs | 8x H100 | $23.92 | $1,196 |
| GPUBrazil | 8x H100 | $22.40 | $1,120 |
GPUBrazil saves $3,796 (77%) compared to AWS on this single training run!
Try GPUBrazil Risk-Free
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Start Free Trial →Availability & Reliability
GPU availability has been a major issue in 2024-2025. Here's how providers stack up:
| Provider | H100 Availability | Queue Time | Reliability |
|---|---|---|---|
| AWS | Limited (quotas) | Days-weeks | 99.9% |
| GCP | Limited (quotas) | Days-weeks | 99.9% |
| Lambda Labs | Often sold out | Hours-days | 99% |
| RunPod | Variable | Minutes-hours | 95% |
| GPUBrazil | Good (FLEX) / Guaranteed (PREMIUM) | Instant-minutes | 99.5% |
Our Recommendation
For most ML practitioners in 2025, here's our advice:
- Default choice: GPUBrazil — Best price-performance, instant access, no BS
- Enterprise with AWS lock-in: Stick with AWS but optimize with Reserved Instances
- Need TPUs: Google Cloud is your only real option
- Hobbyist/budget: Vast.ai for non-critical work, GPUBrazil for anything important
The hyperscalers had their time, but specialized GPU clouds now offer better prices, faster deployment, and simpler pricing. There's rarely a good reason to overpay 3-4x for the same hardware.
Conclusion
The GPU cloud landscape has matured significantly. While AWS, GCP, and Azure still dominate enterprise, cost-conscious teams are flocking to specialized providers.
GPUBrazil offers the best combination of price, availability, and features for ML workloads in 2025. With H100s at $2.80/hour and instant deployment, it's hard to justify paying 4x more elsewhere.
Try GPUBrazil free and see the difference yourself.