GPU Cloud Pricing Comparison: AWS vs GCP vs Azure

The demand for cloud GPUs has exploded with the rise of AI and machine learning. Whether you are training large language models, running inference workloads, or processing computer vision tasks, choosing the right cloud GPU provider can mean the difference between a manageable bill and a budget-breaking disaster.

This guide compares GPU pricing across the three major cloud providers, Amazon Web Services (AWS), Google Cloud Platform (GCP), and Microsoft Azure, helping you make informed decisions about where to run your GPU workloads.

Understanding GPU Instance Types

Each cloud provider offers various GPU options, from entry-level T4s to cutting-edge H100s. Here is how the major GPU types compare:

GPU Model	Memory	Use Case	Relative Performance
NVIDIA T4	16 GB	Inference, light training	1x (baseline)
NVIDIA V100	16-32 GB	Training, HPC	3-4x
NVIDIA A10G	24 GB	Inference, graphics	2-3x
NVIDIA A100	40-80 GB	Large model training	10-15x
NVIDIA H100	80 GB	LLM training, cutting-edge AI	20-30x

AWS GPU Pricing

AWS GPU Instance Types:

Instance	GPU	GPU Count	On-Demand $/hr	Spot $/hr (typical)
g4dn.xlarge	T4	1	$0.526	$0.16-0.21
g5.xlarge	A10G	1	$1.006	$0.30-0.40
p4d.24xlarge	A100 40GB	8	$32.77	$9.80-13.00
p5.48xlarge	H100	8	$98.32	Limited availability

AWS Pricing Models:

• On-Demand: Pay by the second with no commitment
• Spot Instances: Up to 90% discount but can be interrupted
• Reserved Instances: 1-3 year commitment for 30-60% savings
• Savings Plans: Flexible commitments with similar discounts

Google Cloud GPU Pricing

GCP GPU Options:

GPU Type	Memory	On-Demand $/hr (GPU only)	Preemptible $/hr
NVIDIA T4	16 GB	$0.35	$0.11
NVIDIA V100	16 GB	$2.48	$0.74
NVIDIA A100 40GB	40 GB	$3.67	$1.10
NVIDIA A100 80GB	80 GB	$4.40	$1.32
NVIDIA H100	80 GB	$10.80	$3.24

GCP Alternative: TPUs

Google also offers Tensor Processing Units (TPUs), custom AI accelerators that can be more cost-effective for certain workloads, especially JAX and TensorFlow models.

TPU Type	On-Demand $/hr	Best For
TPU v2	$4.50	ML training basics
TPU v3	$8.00	Larger models
TPU v4	$12.88	LLM training
TPU v5e	$1.20	Cost-effective inference

Azure GPU Pricing

Azure GPU VM Series:

VM Size	GPU	GPU Count	$/hr (Pay-as-you-go)
NC4as T4 v3	T4	1	$0.526
NC6s v3	V100	1	$3.06
NC24ads A100 v4	A100 80GB	1	$3.67
ND96asr A100 v4	A100 80GB	8	$27.20
ND H100 v5	H100	8	$98.32

Azure Pricing Options:

• Pay-as-you-go: Standard on-demand pricing
• Spot VMs: Up to 90% discount, can be evicted
• Reserved Instances: 1-3 year terms, up to 72% savings
• Azure Hybrid Benefit: Additional savings with Windows Server licenses

Head-to-Head Comparison

Let us compare equivalent GPU configurations across providers:

Beyond the Big Three: Alternative Providers

Several alternative cloud GPU providers offer competitive pricing:

Provider	A100 $/hr	H100 $/hr	Notes
Lambda Labs	$1.29	$2.49	ML-focused, good availability
CoreWeave	$2.21	$4.25	High-performance networking
Paperspace	$3.09	N/A	Easy to use, good for individuals
RunPod	$1.69	$3.89	Serverless GPU options
Vast.ai	$0.80-1.50	$2.00-3.00	Marketplace, variable quality

Cost Optimization Strategies

1. Use Spot/Preemptible Instances

For interruptible workloads like training with checkpointing, spot instances can save 60-90%.

2. Right-Size Your GPUs

Not every task needs an A100. A T4 or V100 may be sufficient and far cheaper.

3. Optimize GPU Utilization

• Batch similar jobs together
• Use smaller batch sizes if memory-limited, not compute-limited
• Implement proper GPU monitoring
• Consider multi-tenancy for inference workloads

4. Leverage Commitments Strategically

• Reserve capacity for baseline workloads
• Use on-demand for peaks
• Consider savings plans for flexibility

5. Geographic Arbitrage

Prices vary by region. If latency is not critical, run workloads in cheaper regions.

Choosing the Right Provider

Choose AWS if:

• You need the widest GPU selection and best availability
• Your organization already uses AWS heavily
• You need enterprise-grade SLAs and support

Choose GCP if:

• You use TensorFlow or JAX and can leverage TPUs
• Preemptible pricing is attractive for your workload
• You want simpler a la carte GPU pricing

Choose Azure if:

• You have Microsoft enterprise agreements
• You want access to Azure OpenAI Service
• You need specific AMD GPU options

Choose Alternative Providers if:

• Cost is your primary concern
• You can tolerate some reliability trade-offs
• Your workloads are relatively simple

Conclusion

GPU cloud pricing is complex and constantly evolving. While headline prices matter, the true cost depends on availability, your specific workload characteristics, and how well you optimize.

Use our GPU Cloud Cost Calculator to estimate your costs across different providers and configurations. Start with understanding your workload requirements, model multiple scenarios, and do not forget about the hidden costs like data transfer and storage.

The GPU cloud market is competitive and increasingly commoditized. Prices generally trend downward over time, and new options emerge regularly. Stay informed, optimize continuously, and do not lock yourself into long commitments unless the savings are substantial.

Whether you are training the next breakthrough AI model or running inference at scale, choosing the right GPU cloud provider and pricing model can save your organization thousands or even millions of dollars per year.