Best GPU Cloud Providers 2026
Which GPU cloud provider has the cheapest H100 instances?
Vast.ai offers the lowest H100 spot prices at ~$1.99/hr, but with variable availability. For reliable capacity, CoreWeave leads at $2.23/hr. Lambda Labs offers $2.49/hr with free egress. Hyperscalers (AWS, GCP, Azure) charge 50-100% premiums ($4-5/hr) but offer enterprise SLAs and compliance certifications.
Key Data Points
- Cheapest H100 (Spot): ~$1.99/hr (Vast.ai)
- Cheapest H100 (On-Demand): $2.23/hr (CoreWeave)
- Hyperscaler Rate (AWS/Azure): $4.10-$4.56/hr
- Egress Costs: Free (Lambda/RunPod) vs $0.09/GB (AWS)
- Enterprise Tier: CoreWeave and Lambda Labs (NVIDIA Elite Partners)
GPU Cloud Provider Comparison
| Provider | H100 Price | A100 Price | Min Commitment | Egress | Availability | Best For |
|---|---|---|---|---|---|---|
| CoreWeaveEnterprise | $2.23/hr | $1.21/hr | None (spot) / 3mo (reserved) | $0.05/GB | Excellent | Large-scale training, reserved capacity |
| Lambda LabsMid-market | $2.49/hr | $1.29/hr | None | Free (1TB/mo) | Good | ML research, startups |
| RunPodCommunity | $2.39/hr | $1.19/hr | None | Free | Variable | Inference, spot workloads |
| Vast.aiMarketplace | $1.99/hr (spot) | $0.89/hr (spot) | None | Free | Variable | Budget-conscious, interruptible |
| Together.aiEnterprise | $3.10/hr | $1.50/hr | None | $0.08/GB | Good | Inference API, fine-tuning |
| AWS (p5)Hyperscaler | $4.10/hr (on-demand) | $3.06/hr | None (spot) / 1yr (reserved) | $0.09/GB | Limited | Enterprise integration, compliance |
| GCP (a3-highgpu)Hyperscaler | $3.98/hr | $2.93/hr | None / 1yr committed use | $0.12/GB | Limited | Vertex AI integration |
| Azure (ND H100)Hyperscaler | $4.56/hr | $3.40/hr | None / 1yr reserved | $0.087/GB | Very Limited | Enterprise Azure stack |
Prices as of January 2026. On-demand rates unless noted. Check GLRI for real-time pricing.
Provider Analysis
CoreWeave
Best for: Large-scale reserved training clusters
- • Kubernetes-native, InfiniBand networking
- • 3-month reservations for ~30% discount
- • Strong availability for multi-node clusters
- • Enterprise SLAs available
Lambda Labs
Best for: ML research and startups
- • Pre-configured ML environments
- • Free egress (1TB/month)
- • No long-term commitments
- • Good developer experience
RunPod
Best for: Inference and spot workloads
- • Serverless GPU option
- • Community cloud (variable quality)
- • Very competitive spot pricing
- • Good for inference endpoints
Hyperscalers (AWS/GCP/Azure)
Best for: Enterprise compliance and integration
- • SOC2, HIPAA, FedRAMP compliance
- • Deep integration with cloud services
- • Enterprise support SLAs
- • 50-100% price premium
Recommendations by Use Case
Best for Training
CoreWeave
Reserved H100 clusters with InfiniBand. 3-month commitments for best pricing on multi-node training.
Best for Inference
RunPod / Lambda
On-demand scaling, serverless options, and competitive pricing for production inference.
Best for Enterprise
AWS / GCP
Compliance certifications, enterprise SLAs, and deep integration with existing cloud infrastructure.
Frequently Asked Questions
Why are hyperscalers so much more expensive?
Hyperscalers (AWS, GCP, Azure) charge premiums for: enterprise SLAs, compliance certifications (SOC2, HIPAA, FedRAMP), integration with broader cloud services, and guaranteed capacity. For regulated industries, these premiums are often justified.
Is spot/preemptible pricing worth the risk?
For fault-tolerant workloads (training with checkpoints, batch inference), spot pricing can reduce costs by 50-70%. Not recommended for real-time inference or workloads that cannot handle interruptions.
How do I compare total cost including egress?
For training, egress is minimal (mostly model weights). For inference serving, egress can add 10-20% to costs. Lambda and RunPod offer free egress, which can be significant for high-throughput inference.
Should I reserve capacity or use on-demand?
Reserve if: consistent utilization >60%, multi-month project, need guaranteed availability. Use on-demand if: variable workloads, testing/experimentation, or need flexibility to scale down.
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