Comparison beginner · 3 min read

Modal GPU pricing comparison

Q: Modal GPU pricing comparison

Use Modal for flexible serverless GPU compute with pricing starting around $0.45 per GPU hour for an A10G instance. Modal offers pay-as-you-go GPU access with no upfront fees, making it cost-effective for bursty AI workloads and experimentation.

Quick answer

Use Modal for flexible serverless GPU compute with pricing starting around $0.45 per GPU hour for an A10G instance. Modal offers pay-as-you-go GPU access with no upfront fees, making it cost-effective for bursty AI workloads and experimentation.

VERDICT

For cost-effective, on-demand GPU compute with easy scaling, Modal is the winner due to its transparent hourly pricing and broad GPU support tailored for AI development.

Tool	Key strength	Pricing	API access	Best for
Modal	Serverless GPU with flexible scaling	$0.45–$1.20 per GPU hour depending on GPU type	Yes, via `modal` Python SDK	AI model training, inference, and prototyping
AWS EC2 GPU	Wide GPU variety and ecosystem	Varies $0.50–$3.00+ per GPU hour	Yes, via AWS SDKs	Production workloads, large-scale training
Google Cloud GPU	Integrated with GCP services	Approx. $0.43–$2.50 per GPU hour	Yes, via Google Cloud SDK	ML pipelines, scalable training
Azure GPU VMs	Enterprise-grade GPU VMs	Approx. $0.50–$3.00 per GPU hour	Yes, via Azure SDK	Enterprise AI workloads
RunPod	Simple GPU rental for AI	$0.40–$1.00 per GPU hour	Yes, via `runpod` SDK	Quick GPU access for AI inference

Key differences

Modal offers serverless GPU compute with transparent hourly pricing starting at about $0.45 for an A10G GPU, optimized for AI workloads with easy scaling and deployment via its Python SDK. Unlike traditional cloud providers, Modal abstracts infrastructure management, enabling rapid prototyping and bursty usage without long-term commitments. Pricing varies by GPU type, with higher-end GPUs costing up to $1.20 per hour.

Modal's key advantage is its developer-friendly API and serverless model, contrasting with AWS, GCP, and Azure which require more infrastructure setup and often have higher minimum usage commitments. Modal is ideal for developers needing flexible, on-demand GPU access without managing VM lifecycles.

Side-by-side example

Here is how to launch a GPU serverless function on Modal using the modal Python SDK with an A10G GPU for AI inference:

python

import modal

app = modal.App("gpu-inference")

@modal.function(gpu="A10G", image=modal.Image.debian_slim().pip_install("torch"))
def run_inference(prompt: str) -> str:
    import torch
    # Dummy inference logic
    return f"Processed prompt: {prompt}"

if __name__ == "__main__":
    with modal.runner.deploy_stub(app):
        result = run_inference.remote("Hello from Modal GPU")
        print(result)

output

Processed prompt: Hello from Modal GPU

AWS EC2 GPU equivalent

For comparison, launching a GPU instance on AWS EC2 requires more setup and management. Here is a simplified example using boto3 to start a p3.2xlarge instance with a Tesla V100 GPU:

python

import boto3

ec2 = boto3.client('ec2')

response = ec2.run_instances(
    ImageId='ami-0abcdef1234567890',  # Replace with valid AMI
    InstanceType='p3.2xlarge',
    MinCount=1,
    MaxCount=1,
    KeyName='your-key-pair',
    SecurityGroupIds=['sg-0123456789abcdef0']
)
print(f"Started EC2 instance: {response['Instances'][0]['InstanceId']}")

output

Started EC2 instance: i-0abcd1234efgh5678

When to use each

Use Modal when you need fast, serverless GPU compute with minimal infrastructure overhead and pay only for what you use. It suits AI prototyping, inference, and bursty workloads.

Use traditional cloud GPU VMs (AWS, GCP, Azure) for large-scale, persistent training jobs or when you require deep integration with cloud ecosystems and enterprise features.

Use case	Modal	AWS/GCP/Azure
Quick AI prototyping	Excellent - serverless, fast startup	Less convenient, requires VM setup
Large-scale training	Possible but limited by serverless constraints	Best - scalable, persistent GPU clusters
Cost control for bursty workloads	Best - pay-as-you-go hourly pricing	Can be costly due to minimum usage
Enterprise integration	Limited	Full cloud ecosystem support

Pricing and access

Modal charges approximately $0.45 per hour for an A10G GPU and up to $1.20 per hour for higher-end GPUs like A100. There are no upfront fees or minimum commitments. Access is via the modal Python SDK with simple deployment commands.

GPU Type	Hourly Price (USD)	Access Method
A10G	$0.45	`modal` Python SDK
T4	$0.60	`modal` Python SDK
A100	$1.20	`modal` Python SDK
AWS p3.2xlarge (V100)	$3.06	AWS SDK / Console
GCP A100	$2.50	Google Cloud SDK

✅

Key Takeaways

Modal offers transparent, pay-as-you-go GPU pricing ideal for bursty AI workloads.
Serverless GPU access on Modal reduces infrastructure management compared to traditional cloud VMs.
For large-scale, persistent training, AWS, GCP, or Azure remain the best options.
Modal's Python SDK enables rapid deployment of GPU functions with minimal setup.

Verified 2026-04 · A10G, A100, p3.2xlarge, T4

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