AWS vs Lambda Labs GPU Cloud Cost Comparison
AWS vs Lambda Labs GPU Cloud Cost Comparison
GPU cloud costs can make or break your machine learning project's economics. The difference between providers is staggering—the same A100 GPU can cost $1.10/hour on one platform and $4/hour on another. Choosing the right provider for your use case saves thousands on training runs.
In this guide, you'll learn the 2025 pricing for major GPU cloud providers, understand what you're actually paying for, and calculate costs for your specific training or inference needs. Use our calculator to compare options instantly.
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Enter your GPU requirements and training time to compare costs across AWS, Lambda Labs, RunPod, CoreWeave, and more.
How It Works: GPU Cloud Pricing
GPU cloud providers charge by the hour for virtual machines with attached GPUs. Pricing depends on:
2025 Hourly Rates (A100 80GB):
| Provider | On-Demand | Reserved/Spot |
|----------|-----------|---------------|
| AWS (p4d.24xlarge) | $32.77/hr (8x A100) | ~$20/hr reserved |
| GCP (a2-ultragpu-8g) | $29.39/hr (8x A100) | ~$18/hr spot |
| Lambda Labs | $1.29/hr (1x A100) | $1.10/hr reserved |
| RunPod | $1.24/hr (1x A100) | $0.74/hr spot |
| CoreWeave | $2.21/hr (1x A100) | ~$1.50 reserved |
Why the difference? AWS and GCP bundle expensive networking, storage, and support into their pricing. Dedicated GPU providers offer bare GPUs with less overhead but fewer enterprise features.
Per-GPU cost: AWS charges ~$4.10/GPU/hour on p4d instances. Lambda Labs is $1.29/GPU—3x cheaper for the same hardware.
Step-by-Step Example: Training Cost Comparison
Scenario: Training a large language model requiring 100 A100-GPU-hours.
AWS (using p4d.24xlarge - 8x A100):
Lambda Labs (single A100 instances):
RunPod (spot pricing):
Savings: RunPod saves $335.63 (82%) vs AWS for the same compute.
However, AWS offers faster multi-GPU interconnect (NVLink), which may reduce actual training time for distributed jobs. For single-GPU workloads, the cheap providers win decisively.
Key Factors to Consider
1. Multi-GPU Scaling Needs
AWS and GCP have superior multi-GPU networking (NVLink, InfiniBand) essential for distributed training across 8+ GPUs. Lambda Labs and RunPod are better for single-GPU or embarrassingly parallel workloads.
2. Availability Is a Real Constraint
Cheap GPU clouds often have limited availability. RunPod spot instances can be interrupted. Lambda Labs has waitlists for popular GPUs. AWS has essentially unlimited capacity on-demand.
3. Storage and Egress Add Hidden Costs
AWS charges for EBS storage (~$0.08/GB/month) and data egress (~$0.09/GB). Lambda Labs includes 200GB free storage. For data-heavy workloads, factor in these costs.
4. H100s Are the New Frontier
NVIDIA H100s offer 2-3x A100 performance. Pricing:
For cutting-edge training, H100 availability and pricing should drive provider selection.
Frequently Asked Questions
What is the cheapest GPU cloud for ML training?
RunPod offers the cheapest GPU cloud at $0.74-1.24/hour for A100s (spot pricing). Lambda Labs is $1.10-1.29/hour with better reliability. For reserved capacity, Vast.ai marketplace can go even lower but with variable quality.
Is AWS GPU more expensive than Lambda Labs?
Yes, significantly. AWS charges ~$4/GPU/hour for A100s versus Lambda Labs' $1.29/hour—3x more expensive. AWS justifies this with enterprise features, better networking, and unlimited availability. For budget-conscious projects, Lambda Labs wins.
How much does it cost to train a language model?
Small models (7B parameters): $1,000-10,000 in GPU costs
Medium models (30-70B): $50,000-500,000
Large models (100B+): $1M+
Fine-tuning existing models: $100-5,000
Exact costs depend on training efficiency and provider choice.
Should I use spot instances for ML training?
Spot instances save 50-70% but can be interrupted. Use them for: checkpointing-friendly training, batch inference, and experimentation. Avoid for: time-sensitive training, production inference, or jobs that can't resume from checkpoints.
Which GPU should I choose: A100 vs H100 vs A10?
A10 (24GB): Best for inference, small model training, $1-2/hr
A100 (40/80GB): Standard for training, good for most LLMs, $1-4/hr
H100 (80GB): Latest generation, 2-3x faster than A100, $2.50-12/hr
Choose based on VRAM needs and whether H100's speed justifies the premium.