GPU CloudJanuary 26, 2026•28 min read•By AI Pricing Master

RunPod vs Modal vs AWS: Complete GPU Cloud Comparison (2026)

Comprehensive comparison of RunPod, Modal, and AWS for GPU cloud computing. Detailed pricing, features, performance benchmarks, and cost analysis for AI model hosting in 2026.

RunPod vs Modal vs AWS: Complete GPU Cloud Comparison (2026)

Last Updated: January 2026

💡 Quick Summary: RunPod delivers 60-84% cost savings over AWS for on-demand GPU computing. Modal offers the best Python-native developer experience with sub-4-second cold starts. AWS remains the enterprise choice for compliance-heavy workloads but struggles with hidden egress costs that can add $900+ monthly for data-intensive AI applications.

Quick Comparison Overview
RunPod: The Cost Leader
Modal: Python-Native Serverless
AWS: The Enterprise Choice
Head-to-Head Price Comparison
Real-World Cost Scenarios
Technical Capabilities Compared
Developer Experience
Decision Framework
Frequently Asked Questions
Conclusion

Quick Comparison Overview

Feature	RunPod	Modal	AWS
H100 Price	$1.99-2.99/hr	$3.95/hr	$3.90/GPU/hr
A100 80GB Price	$1.19/hr	$2.50/hr	$3.43/GPU/hr
Billing	Per-second	Per-second	Per-hour
Egress Fees	$0 (Free)	$0 (Free)	$0.09/GB
Cold Start	under 200ms (FlashBoot)	2-4 seconds	Minutes
Free Credits	$5-500 bonus	$30/month	Free tier limited
Scale-to-Zero	✅ Serverless	✅ Native	❌ Complex
Best For	Cost optimization	Developer experience	Enterprise compliance

RunPod: The Cost Leader

RunPod has established itself as the most cost-effective GPU cloud platform with transparent pricing across 32+ GPU types. The platform offers two deployment models: GPU Pods for persistent workloads and Serverless endpoints for scale-to-zero inference.

GPU Pod Pricing

RunPod offers two cloud tiers with different pricing and SLA guarantees:

Community Cloud (Lowest Cost)

GPU Model	VRAM	Hourly Rate	Monthly (24/7)
H200	141GB	$3.59/hr	$2,585
H100 SXM	80GB	$1.99/hr	$1,433
H100 NVL	94GB	$2.59/hr	$1,865
H100 PCIe	80GB	$1.35-1.50/hr	$972-1,080
A100 SXM 80GB	80GB	$1.19-1.99/hr	$857-1,433
A100 PCIe 40GB	40GB	$0.60/hr	$432
L40S	48GB	$0.40/hr	$288
RTX 4090	24GB	$0.20-0.34/hr	$144-245
RTX A6000	48GB	$0.25-0.40/hr	$180-288
RTX 3090	24GB	$0.11-0.20/hr	$79-144
RTX 3080	10GB	$0.10/hr	$72

Secure Cloud (Enterprise SLA)

GPU Model	VRAM	Hourly Rate	SLA
H200	141GB	~$4.31/hr	99.99% uptime
H100 SXM	80GB	$2.69-2.99/hr	99.99% uptime
H100 NVL	94GB	$2.79/hr	99.99% uptime
A100 80GB	80GB	$2.39/hr	99.99% uptime
RTX 4090	24GB	$0.27-0.39/hr	99.99% uptime

⚠️ Spot Instance Warning: Spot instances offer up to 60% additional savings but can be interrupted with only 5-second SIGTERM warnings. Use only for fault-tolerant workloads.

Serverless GPU Pricing

RunPod Serverless bills per-second with two worker types:

GPU Tier	VRAM	Flex ($/sec)	Active ($/sec)	Flex ($/hr)
RTX 4000 Ada	20GB	$0.00019	$0.00013	$0.68
RTX 4090 PRO	24GB	$0.00031	$0.00021	$1.12
L4	24GB	$0.00019	$0.00013	$0.68
L40S	48GB	$0.00053	$0.00037	$1.91
A100 80GB	80GB	$0.00076	$0.00060	$2.74
H100 PRO	80GB	$0.00116	$0.00093	$4.18
H200	141GB	$0.00140	$0.00112	$5.04

Flex Workers scale to zero during idle periods—you pay nothing when not processing requests. Active Workers remain warm to eliminate cold starts at a 20-30% discount.

RunPod's FlashBoot technology achieves cold starts under 200ms for 48% of requests—dramatically faster than traditional container orchestration.

RunPod Storage and Networking

Zero Egress Fees: RunPod charges $0 for data transfer in or out. This is a massive advantage over AWS.

Storage Type	Price
Network Volumes (under 1TB)	$0.07/GB/month
Network Volumes (≥1TB)	$0.05/GB/month
Container Disk (running)	$0.10/GB/month
Container Disk (stopped)	$0.20/GB/month

RunPod Pros and Cons

Pros:

✅ Lowest GPU prices in the market
✅ Zero egress fees
✅ 32+ GPU types including consumer cards (RTX 4090, 3090)
✅ Per-second billing
✅ FlashBoot for fast cold starts
✅ 50+ pre-built templates
✅ Active Discord community

Cons:

❌ Community Cloud has variable availability
❌ Limited enterprise compliance (SOC 2 Type 1 only)
❌ No managed Kubernetes offering
❌ Documentation gaps for edge cases

Modal: Python-Native Serverless

Modal has earned unicorn status with a $1.1 billion valuation (September 2025 Series B), validating their developer-first approach. The platform eliminates YAML configurations and Kubernetes complexity with a Python-native SDK.

Per-Second GPU Pricing

GPU Model	VRAM	Per Second	Per Hour
NVIDIA B200	192GB	$0.001736	$6.25
NVIDIA H200	141GB	$0.001261	$4.54
NVIDIA H100	80GB	$0.001097	$3.95
NVIDIA A100 80GB	80GB	$0.000694	$2.50
NVIDIA A100 40GB	40GB	$0.000583	$2.10
NVIDIA L40S	48GB	$0.000542	$1.95
NVIDIA A10G	24GB	$0.000306	$1.10
NVIDIA L4	24GB	$0.000222	$0.80
NVIDIA T4	16GB	$0.000164	$0.59

CPU and Memory Pricing:

CPU: $0.047/core/hour
Memory: $0.008/GiB/hour
No per-invocation fees (unlike AWS Lambda)

ℹ️ Regional Pricing: Non-US regions have 1.25x to 2.5x multipliers. Non-preemptible execution costs 3x standard rates.

Cold Start Performance

Modal's custom lightweight VMs using gVisor achieve 2-4 second cold starts consistently, with GPU-enabled containers spinning up in as little as 1 second. Their FUSE-based lazy-loading filesystem enables near-instant code execution.

Metric	Modal	RunPod Serverless	AWS Lambda
Cold Start (GPU)	2-4 sec	under 200ms-5 sec	N/A (no GPU)
Cold Start (CPU)	under 1 sec	1-2 sec	100ms-1 sec
Scale-to-Zero	Native	Native	Native
Max Concurrency	1,000+	Unlimited	1,000

The Python-Native Advantage

Modal's decorator-based deployment eliminates infrastructure boilerplate:

import modal

app = modal.App()
image = modal.Image.debian_slim().pip_install("torch", "transformers")

@app.function(gpu="H100", image=image)
def inference(prompt: str):
    from transformers import pipeline
    pipe = pipeline("text-generation", model="meta-llama/Llama-3.1-8B")
    return pipe(prompt, max_length=100)

# Deploy with: modal deploy app.py
# Call with: modal run app.py::inference --prompt "Hello"

This approach provides:

Hot reloading during development
Real-time log streaming
Interactive cloud shells for debugging
Local-to-cloud deployment in minutes

Free Tier and Credits

Plan	Monthly Credits	GPU Concurrency	Seats
Starter (Free)	$30	10	3
Team ($250/mo)	$250 included	50	5
Enterprise	Custom	Custom	Unlimited

Startup Program: Up to $25,000 in credits Academic Program: Up to $10,000 in credits

Modal Pros and Cons

Pros:

✅ Best developer experience (Python-native)
✅ Sub-4-second cold starts
✅ Zero egress fees
✅ $30/month free credits
✅ Excellent documentation with 30+ examples
✅ SOC 2 compliance on all plans

Cons:

❌ Higher per-hour rates than RunPod
❌ No consumer GPUs (RTX 4090, 3090)
❌ No Kubernetes support
❌ Regional pricing multipliers
❌ Limited enterprise compliance vs AWS

AWS: The Enterprise Choice

AWS reduced GPU instance pricing by up to 45% in June 2025, but January 2026 saw 15% Capacity Block price increases for H200 instances. AWS remains the go-to for enterprises requiring specific compliance certifications.

EC2 GPU Instance Pricing

P5 Instances (H100)

Instance	GPUs	On-Demand	Spot (~60% off)	Per-GPU/hr
p5.48xlarge	8x H100 80GB	$31.22/hr	~$12.50/hr	$3.90

P5e Instances (H200)

Instance	GPUs	On-Demand	Per-GPU/hr
p5e.48xlarge	8x H200 141GB	$39.80/hr	~$5.00

P4d Instances (A100)

Instance	GPUs	On-Demand	Spot	Per-GPU/hr
p4d.24xlarge	8x A100 40GB	$21.96/hr	~$8.80/hr	$2.75
p4de.24xlarge	8x A100 80GB	$27.45/hr	~$11.00/hr	$3.43

G5 Instances (A10G)

Instance	GPUs	On-Demand	Spot	Best For
g5.xlarge	1x A10G	$1.006/hr	~$0.30/hr	Small inference
g5.2xlarge	1x A10G	$1.212/hr	~$0.36/hr	Medium workloads
g5.12xlarge	4x A10G	$5.672/hr	~$1.70/hr	Multi-GPU
g5.48xlarge	8x A10G	$16.29/hr	~$4.90/hr	Large batch

G4dn Instances (T4)

Instance	GPUs	On-Demand	Spot
g4dn.xlarge	1x T4	$0.526/hr	~$0.16/hr
g4dn.12xlarge	4x T4	$3.912/hr	~$1.17/hr

AWS Savings Plans

Commitment	Discount	Effective H100/hr
On-Demand	0%	$3.90
1-Year Reserved	~25%	$2.93
3-Year Reserved	~45%	$2.15

⚠️ Commitment Risk: AWS Savings Plans require 1-3 year commitments. GPU pricing has dropped 40-60% in the past 18 months—locking in today's rates may be costly if prices continue falling.

The Egress Cost Problem

AWS data transfer fees are the platform's hidden tax:

Data Volume	Egress Price
First 100 GB/month	Free
Next 10 TB	$0.09/GB
Next 40 TB	$0.085/GB
Next 100 TB	$0.07/GB
150 TB+	$0.05/GB

Real Cost Example:

Team transferring 10TB/month of model weights and training data
Egress cost: $900/month (often exceeds compute costs on RunPod)

Cross-region transfers add $0.02/GB, and cross-AZ transfers cost $0.01/GB each direction.

SageMaker and Bedrock

SageMaker Inference Endpoints

Instance	GPUs	Hourly Rate
ml.g5.xlarge	1x A10G	$1.41/hr
ml.g5.12xlarge	4x A10G	$7.09/hr
ml.p4d.24xlarge	8x A100	$37.69/hr
ml.inf2.xlarge	Inferentia2	$0.76/hr

Amazon Bedrock (Per-Token Pricing)

Model	Input (per 1K tokens)	Output (per 1K tokens)
Claude 3.5 Sonnet	$0.003	$0.015
Claude 3.5 Haiku	$0.001	$0.005
Llama 3.1 70B	$0.00099	$0.00099
Llama 3.1 405B	$0.00532	$0.016
Amazon Nova Pro	$0.0008	$0.0032
Amazon Nova Lite	$0.00006	$0.00024

Bedrock's prompt caching reduces costs by up to 90% on cached tokens.

AWS Pros and Cons

Pros:

✅ Most comprehensive compliance (SOC 1/2/3, HIPAA, PCI, FedRAMP)
✅ 99.99% regional SLAs
✅ Deep integration with AWS ecosystem
✅ Spot instances up to 90% off
✅ Reserved pricing competitive at scale
✅ UltraClusters for massive training (20,000+ GPUs)

Cons:

❌ Egress fees add $0.09/GB
❌ Hourly billing (no per-second)
❌ Complex IAM, VPC, security group setup
❌ No consumer GPUs
❌ Cold starts measured in minutes
❌ Steep learning curve

Head-to-Head Price Comparison

Same GPU, Different Prices

H100 80GB (Per Hour)

Provider	On-Demand	Committed	Spot
RunPod	$1.99	N/A	~$1.49
Modal	$3.95	N/A	Auto
AWS	$3.90	$2.15 (3-yr)	~$1.56

A100 80GB (Per Hour)

Provider	On-Demand	Committed
RunPod	$1.19	N/A
Modal	$2.50	N/A
AWS	$3.43	~$1.89 (3-yr)

L40S 48GB (Per Hour)

Provider	On-Demand
RunPod	$0.40
Modal	$1.95
AWS	Limited availability

Total Cost of Ownership (100 hrs/month)

Component	RunPod	Modal	AWS
H100 Compute	$199-299	$395	$390
Egress (1TB)	$0	$0	$90
Storage (500GB)	$35	Included	$50
Total	$234-334	~$395	$530+

Winner by Use Case:

Lowest cost: RunPod
Best DX: Modal
Enterprise compliance: AWS

Real-World Cost Scenarios

Llama 405B Inference

Running Meta's largest open model requires significant GPU resources:

Platform	Configuration	Hourly	Monthly (100hrs)
RunPod Pod	2x H100 80GB	$3.98	$398
RunPod Serverless	H100 PRO	$4.18 (active)	~$200-300 (variable)
Modal	2x H100	$7.90	$790
AWS	p5.48xlarge (shared)	$7.81	$781 + egress

Recommendation: For bursty inference (under 30% utilization), RunPod Serverless provides the best economics.

Stable Diffusion Deployment

Image generation benefits from RunPod's consumer GPU availability:

GPU	RunPod	Modal	AWS Equivalent
RTX 4090	$0.34/hr	❌ N/A	❌ N/A
L40S	$0.40/hr	$1.95/hr	~$1.10 (A10G)
A100 40GB	$0.60/hr	$2.10/hr	$2.75/hr

Cost for 10,000 images/month (assuming 3 sec/image = 8.3 hrs):

RunPod RTX 4090: $2.83
Modal L40S: $16.21
AWS g5.xlarge: $8.35 + egress

Fine-Tuning a 7B Model

LoRA fine-tuning typically requires 4-8 hours on a capable GPU:

Platform	GPU	Duration	Total Cost
RunPod	RTX 4090	6 hours	$2.04
RunPod	A100 40GB	4 hours	$2.40
Modal	A100 40GB	4 hours	$8.40
AWS	g5.2xlarge	6 hours	$7.27

Per-second billing on RunPod and Modal eliminates waste from partial hours.

Serving 1 Million Requests/Month

Production inference API (assuming 3 sec/request average = 833 GPU-hours):

Platform	GPU	Compute	Egress (10TB)	Total
RunPod Serverless	L40S	~$1,800	$0	~$1,800
Modal	L40S	~$1,625	$0	~$1,625
AWS SageMaker	g5.xlarge	~$1,175	$900	~$2,075

Technical Capabilities Compared

Supported GPU Types

GPU	RunPod	Modal	AWS
B200 (192GB)	✅ $5.98/hr	✅ $6.25/hr	Coming 2026
H200 (141GB)	✅ $3.59/hr	✅ $4.54/hr	✅ p5e
H100 (80GB)	✅ $1.99/hr	✅ $3.95/hr	✅ p5
A100 80GB	✅ $1.19/hr	✅ $2.50/hr	✅ p4de
A100 40GB	✅ $0.60/hr	✅ $2.10/hr	✅ p4d
L40S (48GB)	✅ $0.40/hr	✅ $1.95/hr	Limited
A10G (24GB)	✅	✅ $1.10/hr	✅ g5
L4 (24GB)	✅	✅ $0.80/hr	✅ g6
T4 (16GB)	✅	✅ $0.59/hr	✅ g4dn
RTX 4090 (24GB)	✅ $0.34/hr	❌	❌
RTX 3090 (24GB)	✅ $0.11/hr	❌	❌
RTX 3080 (10GB)	✅ $0.10/hr	❌	❌

GPU Selection Winner: RunPod (32+ GPU types including consumer cards)

Container and Orchestration

Feature	RunPod	Modal	AWS
Docker Support	Full	Python-defined	Full
Kubernetes	Limited	No	EKS (full)
Custom Images	Docker Hub, Private	Python Image class	ECR, Docker Hub
Multi-GPU	Up to 8x H100	Multi-GPU functions	Up to 8x per instance
Distributed Training	Instant Clusters	Beta multi-node	UltraClusters
Pre-built Templates	50+	30+ examples	SageMaker JumpStart

Security and Compliance

Certification	RunPod	Modal	AWS
SOC 2 Type 1	✅	✅	✅
SOC 2 Type 2	In progress	✅	✅
SOC 1/3	❌	❌	✅
HIPAA	Secure Cloud	Enterprise	✅ With BAA
PCI DSS	Limited	❌	✅ Level 1
FedRAMP	❌	❌	✅ High
GDPR	✅	✅	✅
ISO 27001	❌	❌	✅

Compliance Winner: AWS (only choice for FedRAMP, PCI Level 1)

Developer Experience

Time to First Deployment

Platform	Setup Time	Learning Curve
Modal	5-10 minutes	Low (Python-native)
RunPod	15-30 minutes	Low-Medium
AWS	Hours to days	High

Deployment Complexity

Modal (Simplest):

pip install modal
modal setup
modal deploy app.py

RunPod (Simple):

# Use web UI or API
# Select template → Configure → Deploy
# Or use runpodctl CLI

AWS (Complex):

# Configure IAM roles
# Set up VPC and security groups
# Request GPU quota increase
# Create instance or SageMaker endpoint
# Configure CloudWatch logging
# Set up auto-scaling

Documentation Quality

Platform	Docs Rating	Highlights
Modal	⭐⭐⭐⭐⭐	Excellent examples, playground
RunPod	⭐⭐⭐⭐	Good templates, active Discord
AWS	⭐⭐⭐	Comprehensive but fragmented

Decision Framework

Quick Decision Matrix

Your Situation	Best Choice
Lowest possible cost	RunPod
Best developer experience	Modal
Enterprise compliance (HIPAA, FedRAMP)	AWS
Consumer GPUs (RTX 4090)	RunPod
Python-native deployment	Modal
under 30% GPU utilization	Modal (scale-to-zero)
>70% sustained utilization	RunPod or AWS Reserved
Existing AWS infrastructure	AWS
Startup with limited budget	RunPod
Academic research	Modal ($10K credits)

By Use Case

Use Case	Recommendation	Why
Hobbyist/Learning	RunPod	$0.11/hr RTX 3090, pre-built templates
Startup MVP	Modal	Fast iteration, $30/mo free
Production Inference	Modal or RunPod Serverless	Scale-to-zero, per-second billing
Model Training	RunPod	Lowest multi-GPU costs
Fine-Tuning	RunPod	RTX 4090 at $0.34/hr
Image Generation	RunPod	RTX 4090 availability
Enterprise API	AWS or Modal	Compliance, SLAs
Regulated Industry	AWS	FedRAMP, HIPAA, PCI

By GPU Utilization

Utilization	Best Option	Monthly Savings vs AWS
under 30%	Modal Serverless	60-80%
30-50%	RunPod Serverless	50-70%
50-70%	RunPod Pods	40-60%
>70%	AWS Reserved or RunPod	20-40%

Frequently Asked Questions

Which is cheaper, RunPod or AWS?

RunPod is 60-84% cheaper than AWS for on-demand GPU computing. An H100 costs $1.99/hr on RunPod vs $3.90/hr on AWS. Additionally, RunPod has zero egress fees while AWS charges $0.09/GB, which can add hundreds of dollars monthly for data-intensive workloads.

Is Modal worth the higher price vs RunPod?

Modal's higher hourly rates ($3.95/hr for H100 vs RunPod's $1.99) are offset by superior developer experience and true scale-to-zero. If your GPU utilization is below 50%, Modal's per-second billing and automatic scaling often result in lower total costs. Modal is worth it for teams prioritizing development velocity over raw compute costs.

Can I run RTX 4090 on AWS or Modal?

No. Neither AWS nor Modal offer consumer GPUs like the RTX 4090 or RTX 3090. RunPod is the only major provider offering these GPUs at $0.34/hr and $0.11/hr respectively. These are excellent for Stable Diffusion, fine-tuning smaller models, and development work.

What are AWS egress fees and how do I avoid them?

AWS charges $0.09/GB for data transferred out to the internet. A team transferring 10TB/month pays $900 in egress alone. To avoid this: use RunPod or Modal (both have zero egress fees), keep data within AWS regions, use CloudFront for content delivery, or consider AWS PrivateLink for inter-service communication.

How fast are cold starts on each platform?

RunPod FlashBoot: under 200ms for 48% of requests
Modal: 2-4 seconds for GPU containers
AWS: Minutes for EC2, seconds to minutes for SageMaker

For latency-sensitive applications, RunPod Serverless with Active Workers or Modal with keep-warm provides the best experience.

Which platform is best for LLM inference?

For cost optimization: RunPod Serverless with H100 PRO at $4.18/hr For developer experience: Modal with easy Python deployment For enterprise compliance: AWS SageMaker or Bedrock For highest throughput: RunPod Pods with dedicated H100s

Do I need Kubernetes for GPU workloads?

No. Both RunPod and Modal abstract away Kubernetes complexity entirely. AWS offers EKS for teams that need Kubernetes, but most AI workloads don't require it. RunPod's Pods and Modal's serverless functions handle scaling automatically.

Which platform has the best compliance certifications?

AWS leads with SOC 1/2/3, HIPAA with BAA, PCI DSS Level 1, FedRAMP High, and ISO 27001. Modal has SOC 2 on all plans. RunPod has SOC 2 Type 1 with Type 2 in progress. For regulated industries (healthcare, finance, government), AWS is often the only option.

How do I choose between serverless and dedicated GPUs?

Use serverless (RunPod Serverless or Modal) when:

GPU utilization is below 50%
Traffic is bursty or unpredictable
You want scale-to-zero cost savings
Cold starts of 1-5 seconds are acceptable

Use dedicated GPUs (RunPod Pods or AWS EC2) when:

GPU utilization exceeds 60%
You need consistent low latency
Running long training jobs
Cost predictability is important

Can startups get free credits?

Yes!

Modal: $30/month free, up to $25,000 for startups
RunPod: $5-500 random bonus on first $10 spend
AWS: $100K+ through AWS Activate for startups
Modal Academic: Up to $10,000 for researchers

Conclusion

The GPU cloud landscape has fundamentally shifted in favor of specialized providers. Here's the definitive recommendation:

Choose RunPod When:

Cost is your primary concern
You need consumer GPUs (RTX 4090, 3090)
Data transfer volumes are high (zero egress fees)
You want pre-built templates for quick deployment
Budget is limited but you need powerful GPUs

Choose Modal When:

Developer experience is the priority
Your team is Python-native
GPU utilization is variable (under 50%)
You want the fastest path from code to production
You value excellent documentation and examples

Choose AWS When:

Compliance requirements mandate it (HIPAA, FedRAMP, PCI)
You have existing AWS infrastructure
You need 99.99% SLAs with enterprise support
Multi-year committed pricing makes sense
You require the deepest cloud service integration

The Bottom Line: Most AI developers and startups should start with RunPod or Modal rather than defaulting to AWS. The 60-84% cost savings, zero egress fees, and superior developer experience make specialized GPU clouds the better choice for the vast majority of workloads. Reserve AWS for compliance-critical production deployments where certifications are non-negotiable.

Deployment Tutorials

RunPod: Deploy Llama 3.1 in 5 Minutes

Step 1: Create Account and Add Credits

# Sign up at runpod.io
# Add minimum $10 (get $5-500 bonus)

Step 2: Deploy Using Template

Go to Pods → Deploy
Select "vLLM" template
Choose H100 80GB GPU
Set environment variable: MODEL=meta-llama/Llama-3.1-70B-Instruct
Click Deploy

Step 3: Access Your Endpoint

import requests

response = requests.post(
    "https://your-pod-id-runpod.io/v1/chat/completions",
    json={
        "model": "meta-llama/Llama-3.1-70B-Instruct",
        "messages": [{"role": "user", "content": "Hello!"}],
        "max_tokens": 100
    }
)
print(response.json())

Modal: Deploy a GPU Function

Step 1: Install and Setup

pip install modal
modal setup  # Authenticate with browser

Step 2: Create Your App

# app.py
import modal

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

image = modal.Image.debian_slim(python_version="3.11").pip_install(
    "torch",
    "transformers",
    "accelerate",
    "bitsandbytes"
)

@app.function(
    gpu="H100",
    image=image,
    timeout=300,
    container_idle_timeout=60
)
def generate(prompt: str, max_tokens: int = 100):
    from transformers import AutoModelForCausalLM, AutoTokenizer
    import torch
    
    model_id = "meta-llama/Llama-3.1-8B-Instruct"
    
    tokenizer = AutoTokenizer.from_pretrained(model_id)
    model = AutoModelForCausalLM.from_pretrained(
        model_id,
        torch_dtype=torch.float16,
        device_map="auto"
    )
    
    inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
    outputs = model.generate(**inputs, max_new_tokens=max_tokens)
    
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

@app.local_entrypoint()
def main():
    result = generate.remote("Explain quantum computing in simple terms:")
    print(result)

Step 3: Deploy and Run

# Test locally
modal run app.py

# Deploy as persistent endpoint
modal deploy app.py

# Your endpoint is now live at:
# https://your-username--llama-inference-generate.modal.run

AWS: Deploy on EC2 (Detailed)

Step 1: Request GPU Quota

# AWS Console → Service Quotas → EC2
# Request increase for "Running On-Demand P instances"
# Wait 24-48 hours for approval

Step 2: Launch Instance

# Using AWS CLI
aws ec2 run-instances \
    --image-id ami-0abcdef1234567890 \
    --instance-type p5.48xlarge \
    --key-name your-key \
    --security-group-ids sg-xxxxxxxx \
    --subnet-id subnet-xxxxxxxx \
    --block-device-mappings '[{"DeviceName":"/dev/sda1","Ebs":{"VolumeSize":500}}]'

Step 3: Connect and Setup

ssh -i your-key.pem ubuntu@your-instance-ip

# Activate PyTorch environment
source activate pytorch

# Install vLLM
pip install vllm

# Start server
vllm serve meta-llama/Llama-3.1-70B-Instruct \
    --tensor-parallel-size 8 \
    --port 8000

Step 4: Configure Security Group

# Allow inbound traffic on port 8000
aws ec2 authorize-security-group-ingress \
    --group-id sg-xxxxxxxx \
    --protocol tcp \
    --port 8000 \
    --cidr 0.0.0.0/0

Performance Benchmarks

Inference Throughput (Tokens/Second)

Model	RunPod H100	Modal H100	AWS p5
Llama 3.1 8B	180 tok/s	175 tok/s	170 tok/s
Llama 3.1 70B	45 tok/s	43 tok/s	42 tok/s
Mistral 7B	210 tok/s	205 tok/s	200 tok/s
Qwen 2.5 72B	40 tok/s	38 tok/s	37 tok/s

Benchmarks using vLLM with default settings, single H100 GPU

Cold Start Comparison

Scenario	RunPod	Modal	AWS
CPU Container	1-2 sec	under 1 sec	30-60 sec
GPU Container (no model)	2-5 sec	2-4 sec	2-5 min
GPU + 7B Model Load	15-30 sec	20-40 sec	3-8 min
GPU + 70B Model Load	60-120 sec	90-180 sec	5-15 min
With FlashBoot/Keep-Warm	under 200ms	1-2 sec	N/A

Image Generation Speed (Stable Diffusion XL)

GPU	RunPod	Modal	AWS
RTX 4090	2.1 sec/img	N/A	N/A
L40S	2.8 sec/img	2.9 sec/img	N/A
A100 40GB	3.2 sec/img	3.3 sec/img	3.5 sec/img
A10G	5.5 sec/img	5.6 sec/img	5.8 sec/img

512x512 image, 30 inference steps

Cost Optimization Strategies

1. Right-Size Your GPU

Don't overpay for VRAM you don't need:

Model Size	Minimum VRAM	Recommended GPU	RunPod Cost
7B (FP16)	14GB	RTX 4090 (24GB)	$0.34/hr
7B (INT4)	4GB	RTX 3080 (10GB)	$0.10/hr
13B (FP16)	26GB	L40S (48GB)	$0.40/hr
34B (FP16)	68GB	A100 80GB	$1.19/hr
70B (FP16)	140GB	2x A100 80GB	$2.38/hr
70B (INT4)	35GB	L40S (48GB)	$0.40/hr

2. Use Quantization

INT4 quantization reduces VRAM by 75% with minimal quality loss:

from transformers import AutoModelForCausalLM, BitsAndBytesConfig
import torch

quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_compute_dtype=torch.float16
)

model = AutoModelForCausalLM.from_pretrained(
    "meta-llama/Llama-3.1-70B-Instruct",
    quantization_config=quantization_config,
    device_map="auto"
)
# Now fits on single 24GB GPU instead of 2x 80GB

3. Leverage Spot/Preemptible Instances

Platform	Spot Savings	Interruption Notice
RunPod	Up to 60%	5 seconds
Modal	N/A	Auto-managed
AWS	Up to 90%	2 minutes

Best for: Training jobs with checkpointing, batch processing, development

4. Implement Request Batching

# Batch multiple requests for higher throughput
from vllm import LLM, SamplingParams

llm = LLM(model="meta-llama/Llama-3.1-8B-Instruct")

prompts = [
    "What is machine learning?",
    "Explain neural networks",
    "What is deep learning?",
    # ... batch up to 32 prompts
]

outputs = llm.generate(prompts, SamplingParams(max_tokens=100))
# 3-5x higher throughput than sequential requests

5. Use Caching Effectively

KV Cache for Repeated Prefixes:

# vLLM automatic prefix caching
llm = LLM(
    model="meta-llama/Llama-3.1-8B-Instruct",
    enable_prefix_caching=True  # Reuse KV cache for common prefixes
)

Response Caching for Identical Queries:

import hashlib
from functools import lru_cache

@lru_cache(maxsize=10000)
def cached_inference(prompt_hash):
    return model.generate(prompt)

def generate_with_cache(prompt):
    prompt_hash = hashlib.md5(prompt.encode()).hexdigest()
    return cached_inference(prompt_hash)

Migration Guide

Moving from AWS to RunPod

Step 1: Export Your Model

# On AWS instance
aws s3 cp /models/your-model s3://your-bucket/models/ --recursive

Step 2: Create RunPod Network Volume

# RunPod Console → Storage → Create Network Volume
# Size: Match your model size + 20%

Step 3: Transfer Model to RunPod

# On RunPod pod with volume attached
pip install awscli
aws configure  # Enter your credentials
aws s3 cp s3://your-bucket/models/ /workspace/models/ --recursive

Step 4: Update Your Application

# Change endpoint from AWS
# OLD: response = requests.post("https://your-sagemaker-endpoint.aws.com/invocations")
# NEW: response = requests.post("https://your-pod-id.runpod.io/v1/completions")

Moving from RunPod to Modal

Step 1: Convert Docker to Modal Image

# RunPod Dockerfile
# FROM pytorch/pytorch:2.0.1-cuda11.7-cudnn8-runtime
# RUN pip install transformers accelerate
# COPY model/ /app/model/

# Modal equivalent
image = modal.Image.from_registry(
    "pytorch/pytorch:2.0.1-cuda11.7-cudnn8-runtime"
).pip_install("transformers", "accelerate")

volume = modal.Volume.from_name("model-volume")

@app.function(gpu="A100", image=image, volumes={"/model": volume})
def inference(prompt):
    # Your inference code
    pass

Step 2: Upload Model to Modal Volume

import modal

volume = modal.Volume.from_name("model-volume", create_if_missing=True)

@app.function(volumes={"/model": volume})
def upload_model():
    # Download from HuggingFace or transfer from S3
    from huggingface_hub import snapshot_download
    snapshot_download("meta-llama/Llama-3.1-8B-Instruct", local_dir="/model")
    volume.commit()

Troubleshooting Common Issues

RunPod Issues

Problem: Pod stuck in "Starting" state

# Solution 1: Check GPU availability in region
# Try different region or GPU type

# Solution 2: Reduce container disk size
# Large disks take longer to provision

# Solution 3: Use Community Cloud instead of Secure Cloud
# More availability but less SLA guarantee

Problem: Out of memory (OOM) errors

# Solution: Enable memory optimization
import torch
torch.cuda.empty_cache()

# For vLLM
# vllm serve model --gpu-memory-utilization 0.85  # Leave headroom

Modal Issues

Problem: Function timeout

# Increase timeout (default 300s)
@app.function(gpu="H100", timeout=3600)  # 1 hour
def long_running_task():
    pass

Problem: Import errors in cloud

# Ensure all dependencies in image
image = modal.Image.debian_slim().pip_install(
    "torch",
    "transformers",
    "accelerate",
    "sentencepiece",  # Often forgotten
    "protobuf",       # Required by some models
)

AWS Issues

Problem: Insufficient capacity

# Solution 1: Try different AZ
aws ec2 run-instances --placement AvailabilityZone=us-east-1b

# Solution 2: Use Capacity Reservations
aws ec2 create-capacity-reservation \
    --instance-type p5.48xlarge \
    --instance-count 1

# Solution 3: Use Spot with multiple instance types

Problem: High egress costs

# Solution 1: Use S3 Transfer Acceleration
aws s3 cp file.tar s3://bucket/ --region us-east-1

# Solution 2: Enable VPC endpoints
# Eliminates inter-region transfer fees

# Solution 3: Compress data before transfer
tar -czvf models.tar.gz models/

Pricing Calculator Examples

Example 1: Startup with Variable Traffic

Scenario: 50,000 requests/month, average 2 seconds GPU time each

Total GPU time: 50,000 x 2 sec = 100,000 seconds = 27.8 hours

RunPod Serverless (L40S):
  27.8 hours x $1.91/hr = $53.10/month

Modal (L40S):
  27.8 hours x $1.95/hr = $54.21/month

AWS SageMaker (g5.xlarge):
  Must pay for always-on: 730 hours x $1.41/hr = $1,029/month
  (Or complex auto-scaling setup)

Winner: RunPod or Modal (95% savings vs AWS always-on)

Example 2: Production API with High Volume

Scenario: 1,000,000 requests/month, 3 seconds average

Total GPU time: 1,000,000 x 3 sec = 3,000,000 seconds = 833 hours

RunPod Pod (A100 80GB, dedicated):
  833 hours x $1.19/hr = $991/month

Modal (A100 80GB):
  833 hours x $2.50/hr = $2,083/month

AWS EC2 (p4de, reserved 1-year):
  833 hours x $1.89/hr = $1,574/month + egress

Winner: RunPod (saves $583-1,092/month)

Example 3: Training Job

Scenario: Fine-tune 7B model, 24-hour job

RunPod (RTX 4090):
  24 hours x $0.34/hr = $8.16

Modal (A100 40GB):
  24 hours x $2.10/hr = $50.40

AWS (g5.2xlarge):
  24 hours x $1.21/hr = $29.04

Winner: RunPod (saves $20-42)

Related Tools

GPU Cloud Pricing Calculator — Compare costs across all providers
LLM API Pricing Calculator — Estimate inference costs
Self-Hosting ROI Calculator — Should you self-host or use cloud?

Prices verified January 2026. GPU cloud pricing changes frequently—always verify current rates on provider websites before making infrastructure decisions.

Tags:

#runpod#modal#aws gpu#gpu cloud#serverless gpu#ai infrastructure#cloud computing#h100 pricing#a100 pricing#ml infrastructure

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