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A100 cloud comparison · April 2026
The cost-effective workhorse for ML — 9 clouds with A100 40GB and 80GB compared. From $1.09/h to $3.67/h for identical hardware.
The NVIDIA A100 remains the workhorse for ML in 2026. While H100 has more raw FP8 throughput, A100 wins on cost-effectiveness for fine-tuning models up to 70B parameters, embedding generation, image training and most research workloads.
9 clouds offer A100 on-demand. Price varies 3.4× — from $1.09/h (Vast.ai community 80GB) to $3.67/h (AWS on-demand). Availability is significantly better than H100; stockouts are rare except on hyperscalers during peak hours.
Best value picks: RunPod and Lambda Labs for production reliability, Vast.ai for batch training, Hetzner GPU for EU/GDPR compliance.
| Provider | Starting Price | Top GPUs | Highlights | Rating | CTA |
|---|---|---|---|---|---|
| Vast.ai Editor's Choice | from $0.10/h | RTX 3090, RTX 4090, A100 ≤80GB |
| ★★★★☆ | View pricing |
| RunPod Editor's Choice | from $0.16/h | RTX A5000, RTX 3090, RTX 4090 ≤80GB |
| ★★★★★ | View pricing |
| Paperspace | from $0.45/h | A100, A6000, RTX 4000 ≤80GB |
| ★★★★☆ | View pricing |
| OVH GPU | from €0.45/h | T4, V100, A100 ≤80GB |
| ★★★★☆ | View pricing |
| AWS GPU (EC2) | from $0.526/h | T4, A100, H100 ≤80GB |
| ★★★★☆ | View pricing |
| Azure GPU (NC T4/A100) | from $0.526/h | T4, A100, H100 ≤80GB |
| ★★★★☆ | View pricing |
| Lambda Labs Editor's Choice | from $0.69/h | Quadro RTX 6000, A100 40GB, A100 80GB ≤80GB |
| ★★★★★ | View pricing |
| CoreWeave | from $1.25/h | L40S, H100 SXM, A100 SXM ≤80GB |
| ★★★★☆ | View pricing |
| Google Cloud GPU | from $3.67/h | A100 40GB, A100 80GB, H100 ≤80GB |
| ★★★★☆ | View pricing |
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For Llama-3 8B / Mistral 7B / Stable Diffusion training: 40GB is sufficient and ~30% cheaper. For Llama-3 70B QLoRA, video AI, or batch sizes >32 on 13B models: 80GB. The price gap shrinks further with FlashAttention-3 and 8-bit optimizers.
Choose A100 for: fine-tuning models ≤70B with QLoRA, ML research, image generation, RAG/embedding pipelines, and any workload not bottlenecked by FP8/Transformer Engine. H100 is only worth the premium for full fine-tunes of large models, multi-node training, or production inference of >70B models.
Vast.ai community at $1.09/h (interruptible) is the absolute cheapest. For reliable production: RunPod Secure at $1.79/h or Lambda Labs at $1.79/h. AWS p4d on-demand is $3.67/h — avoid unless you need SageMaker integration.
Yes — A100 is the cost-effective default for most ML workloads. H100 dominates only large-scale training (≥70B full fine-tune, multi-node) and high-throughput FP8 inference. For ~80% of practical ML, A100 80GB is the better buy.
Hetzner GPU offers A100 PCIe in Germany and Finland at €0.35/h — the cheapest A100 in Europe. OVH (FR/DE) offers A100 at €0.54/h. Both are GDPR-compliant. CoreWeave also has EU H100/A100 capacity.
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