Evaluation Criteria (Weighted by Importance)
| Criterion | Weight | What to Look For |
|---|---|---|
| GPU Pricing | High | Compare on-demand, reserved, spot. Same GPU can cost 2–4× more at hyperscalers. |
| GPU Availability | High | Can you get GPUs when you need them? Ask about capacity guarantees. |
| Egress Costs | Medium–High | Data leaving the cloud costs $0.05–0.09/GB. Lambda offers 10TB free/mo. |
| SLA & Uptime | Medium | What is the GPU availability SLA? What compensation for downtime? |
| Compliance | Situational | SOC2, HIPAA, FedRAMP required? Limits to hyperscalers + select specialized. |
| Network Performance | Medium | Inter-GPU bandwidth for distributed training. InfiniBand vs Ethernet. |
| Support Quality | Low–Medium | Response time, technical depth. Larger providers have slower support. |
| Region/Location | Low–Medium | Data sovereignty requirements, latency to users, electricity cost. |
Provider Tiers in 2026
Tier 1: Hyperscalers (AWS, GCP, Azure)
Best for: Compliance-bound workloads (HIPAA, FedRAMP, SOC2), teams already deep in their ecosystem, enterprise with negotiated pricing.
Price premium: 2–4× vs specialized providers for equivalent GPU compute.
When the premium is worth it: You need IAM integration, native Kubernetes operators, compliance certifications, and are willing to pay for the convenience.
Tier 2: GPU Specialists (CoreWeave, Lambda, Crusoe)
Best for: Pure GPU compute without compliance requirements. Training and inference for teams that can manage their own infrastructure.
Price vs hyperscaler: 40–60% savings. CoreWeave H100: $2.23/hr vs AWS: ~$3.90/hr.
CoreWeave: NVIDIA preferred partner, direct allocation, excellent networking. Best for distributed training at scale.
Lambda Labs: Simple pricing, 10TB/mo free egress, good for research teams. Strong H100 availability.
Tier 3: GPU Marketplaces (Vast.ai, RunPod)
Best for: Budget-maximizing training jobs, experiments, teams comfortable with variable availability.
Price vs hyperscaler: 50–80% savings on spot/interruptible capacity.
Tradeoff: Less enterprise support, availability varies, compliance options limited.
Match Provider to Workload
| Workload | Recommended Provider(s) | Why |
|---|---|---|
| LLM pretraining (> 100 GPUs) | CoreWeave | InfiniBand networking, direct NVIDIA allocation, scale |
| Fine-tuning, smaller training | Lambda, RunPod, Vast.ai | Cost efficiency, good H100/A100 availability |
| Production inference (SLA required) | CoreWeave (reserved), AWS (compliance) | Availability guarantees, SLAs |
| Batch inference / embeddings | Vast.ai, RunPod (spot) | Maximum cost efficiency, fault-tolerant |
| Healthcare AI (HIPAA) | AWS, Azure, GCP | HIPAA BAA available |
| Federal / Government (FedRAMP) | AWS GovCloud, Azure Government | FedRAMP authorization |
Provider Evaluation Checklist
- ☐ Price compare: Use GridStackHub comparison for your GPU model — on-demand, reserved, spot
- ☐ Egress costs: Calculate total egress for your dataset size. Ask for current rates + free tier
- ☐ Compliance check: List your compliance requirements (HIPAA, SOC2, etc.) before evaluating providers
- ☐ Availability test: Request a capacity test: can they provision your target GPU count on 1 hour notice?
- ☐ Network performance: Ask inter-GPU bandwidth for distributed training (InfiniBand vs Ethernet matters above 8 GPUs)
- ☐ SLA review: Read the SLA for GPU availability, support response time, and compensation terms
- ☐ Billing terms: Minimum commitment? Metering granularity? (hourly vs per-second billing)
- ☐ Support test: Submit a technical question before committing — evaluate response speed and quality
After evaluating these criteria, compare real-time prices: GPU Cloud Pricing Comparison →
Frequently Asked Questions
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