Cloud GPU Cost Calculator
Compare GPU cloud costs across AWS, GCP, Azure, Lambda, and RunPod for AI workloads. Enter values for instant results with step-by-step formulas.
Reviewed by Daniel Agrici, Founder & Lead Developer
Formula
Monthly Cost = (Hourly Rate x Hours/Day x Days/Month x Number of GPUs) + Storage Cost + Data Transfer Cost
The total monthly cloud GPU cost combines compute charges based on hourly GPU rates and usage hours, plus storage fees for data and model weights, plus data transfer egress charges. Each provider sets different rates for each component.
Worked Examples
Example 1: Startup Training a Computer Vision Model
Problem:A startup needs 4 A100 GPUs running 10 hours/day for 20 days/month on AWS. They use 2TB storage and 500GB data transfer. What is the monthly cost?
Solution:Compute: $32.77/hr x 10 hrs x 20 days x 4 GPUs = $26,216\nStorage: 2 TB x $23/TB = $46\nData transfer: 500 GB x $0.09/GB = $45\nTotal monthly: $26,216 + $46 + $45 = $26,307
Result:Monthly cost: $26,307 | Annual cost: $315,684 | On RunPod same config: $15,066/mo (43% savings)
Example 2: Comparing Providers for Inference Workload
Problem:A company runs 2 T4 GPUs 24/7 for inference serving. Compare AWS vs RunPod monthly costs with 500GB storage and 1TB transfer.
Solution:AWS: $3.91/hr x 24 hrs x 30 days x 2 GPUs = $5,630.40 + $11.50 (storage) + $90 (transfer) = $5,731.90\nRunPod: $1.68/hr x 24 hrs x 30 days x 2 GPUs = $2,419.20 + $3.50 (storage) + $30 (transfer) = $2,452.70\nSavings: $5,731.90 - $2,452.70 = $3,279.20/month
Result:AWS: $5,732/mo | RunPod: $2,453/mo | Savings: $3,279/mo (57% less on RunPod)
Frequently Asked Questions
How do cloud GPU costs differ between major providers?
Cloud GPU costs vary significantly across providers due to differences in infrastructure scale, pricing strategies, and target markets. AWS, GCP, and Azure typically charge premium rates because they offer extensive managed services, global availability zones, and enterprise-grade SLAs. Smaller providers like Lambda Labs and RunPod can offer the same GPU hardware at 40-60% lower rates because they operate with leaner infrastructure and fewer managed services. The trade-off is that budget providers may have limited availability during peak demand periods, fewer regions, and less comprehensive support options.
Which GPU should I choose for AI model training?
The optimal GPU depends on your model size and training requirements. The NVIDIA H100 is the current top-tier choice for large language model training, offering superior performance with its Transformer Engine and 80GB HBM3 memory. The A100 remains excellent for most deep learning workloads and costs roughly half the H100 price. For fine-tuning smaller models or running inference, the A10G provides strong price-performance. The T4 is ideal for development, testing, and lightweight inference tasks. Consider starting with a less expensive GPU for prototyping and only scaling to H100s when you need maximum training throughput.
How can I reduce my cloud GPU costs?
Several strategies can dramatically reduce cloud GPU expenses. First, use spot or preemptible instances which offer 60-90% discounts for workloads that can handle interruptions. Second, implement automatic shutdown scripts so GPUs are not running idle during off-hours. Third, use mixed-precision training with FP16 or BF16 to reduce memory requirements and potentially use fewer or cheaper GPUs. Fourth, consider reserved instances or committed use discounts for predictable workloads, which can save 30-50% over on-demand pricing. Finally, optimize your code and batch sizes to maximize GPU utilization during active training runs.
What is the difference between on-demand and spot GPU pricing?
On-demand pricing lets you use GPU instances anytime with no commitment, paying a fixed hourly rate with guaranteed availability. Spot pricing (called Preemptible on GCP and Spot on AWS/Azure) offers the same hardware at steep discounts of 60-90% off on-demand rates, but the provider can reclaim your instance with short notice when demand spikes. Spot instances work well for training jobs with checkpointing enabled, batch processing, and distributed training that can resume after interruption. They are not suitable for real-time inference serving or time-critical workloads where downtime is unacceptable.
References
Reviewed by Daniel Agrici, Founder & Lead Developer · Editorial policy