LearnFor BusinessGPU Cloud Pricing
For Business
10 min read
Updated Dec 2025

GPU Cloud Pricing Guide 2025AWS vs GCP vs Azure vs Griddly

GPU cloud costs can make or break your AI project. This comprehensive guide compares pricing across major providers, reveals hidden costs, and shows you how to cut your GPU bill by 50-70%.

70%
A100 Savings
Griddly vs AWS
80%
H100 Savings
Griddly vs AWS
8+
Providers
Compared
4
Hidden Costs
Revealed
G
Griddly Team
Updated December 2025

Table of Contents

Market Overview

The GPU cloud market has exploded with the AI boom. In 2025, demand for datacenter GPUs far exceeds supply, leading to high prices and long waitlists at major cloud providers.

Key Market Trends (2025)

  • • H100 demand far exceeds supply — expect waitlists at major providers
  • • A100 pricing has stabilized but remains expensive at hyperscalers
  • • DePIN alternatives (Griddly, Akash) offer 50-70% savings
  • • Consumer GPUs (RTX 4090) increasingly viable for inference

The good news: competition is increasing, and new options like DePIN networks are disrupting the market with significantly lower prices.

NVIDIA A100 Pricing

The A100 remains the workhorse of AI training. Here's how pricing compares across providers (December 2025):

ProviderGPU ConfigHourlyMonthly*Note
AWS (p4d.24xlarge)8x A100 80GB$32.77$23,594On-demand
AWS Spot8x A100 80GB$9.83$7,078Interruptible
Google Cloud1x A100 40GB$2.93$2,110On-demand
Google Cloud Spot1x A100 40GB$0.88$634Preemptible
Azure1x A100 80GB$3.67$2,642On-demand
Lambda Labs1x A100 80GB$1.10$792On-demand
Vast.ai1x A100 80GB$0.90$648Variable
Griddly
Best Value
1x A100 80GB$0.80$576On-demand
*Monthly estimates based on 720 hours (24/7 usage). Actual costs vary.

A100 Savings Summary

Griddly offers A100 80GB at $0.80/hour — that's 73% cheaper than AWS on-demand and 27% cheaper than Lambda Labs.

NVIDIA H100 Pricing

The H100 is the most sought-after GPU for LLM training. Availability is limited, and prices vary wildly:

ProviderGPU ConfigHourlyMonthly*Note
AWS (p5.48xlarge)8x H100 80GB$98.32$70,790On-demand
Google Cloud1x H100 80GB$~10$7,200Limited access
Azure1x H100 80GB$~12$8,640Preview
Lambda Labs1x H100 80GB$2.49$1,793On-demand
CoreWeave1x H100 80GB$2.23$1,606Reserved
Griddly
Best Value
1x H100 80GB$1.99$1,433On-demand
*Monthly estimates based on 720 hours. H100 availability varies significantly.

H100 Savings Summary

Griddly offers H100 80GB at $1.99/hour — that's 84% cheaper than AWS and 20% cheaper than Lambda Labs.

Consumer GPU Pricing

For inference and smaller workloads, consumer GPUs offer incredible value. Griddly's network includes thousands of RTX 3000/4000 series GPUs:

GPUVRAMHourlyMonthlyBest For
RTX 409024GB$0.45$324Inference, fine-tuning
RTX 408016GB$0.35$252Inference, small models
RTX 309024GB$0.30$216Inference, legacy models
RTX 308010GB$0.20$144Light inference

When to Use Consumer GPUs

An RTX 4090 at $0.45/hr can run Llama 2 7B inference at 50+ tokens/sec. For many use cases, you don't need expensive datacenter GPUs.

Hidden Costs to Watch

GPU hourly rates are just the tip of the iceberg. Watch out for these hidden costs:

$50-500/month

Data Transfer

AWS charges $0.09/GB for data out. Training a large model can cost hundreds in egress fees alone.

$100-1000/month

Storage

Model checkpoints, datasets, and logs add up. EBS/persistent disk costs are often overlooked.

Lock-in risk

Reserved Capacity

Best prices require 1-3 year commitments. Early termination means losing your discount.

20-40% waste

Idle Time

Forgetting to shut down instances or over-provisioning leads to massive waste.

Cost Optimization Strategies

Here's how to cut your GPU cloud bill by 50-70%:

Use Spot/Preemptible Instances

Save 60-70% on interruptible workloads. Use checkpointing to handle interruptions.

60-70%

Right-size Your GPUs

Don't use H100 for inference that runs fine on RTX 4090. Match GPU to workload.

40-80%

Consider DePIN Alternatives

Platforms like Griddly offer 50-70% savings vs hyperscalers with no commitments.

50-70%

Implement Auto-scaling

Scale down during off-hours. A simple cron job can cut costs by 30%+.

30%+

Use Mixed Precision

FP16/BF16 training uses less memory, allowing smaller (cheaper) GPUs.

20-50%

Optimize Data Pipeline

Reduce egress by processing data in-region. Cache frequently accessed datasets.

10-30%

Provider Comparison

Each provider has strengths and weaknesses. Here's a quick comparison:

AWS

Pros
  • Widest selection
  • Enterprise features
  • Global regions
Cons
  • Most expensive
  • Complex pricing
  • High egress costs
Best For

Large enterprises with existing AWS infrastructure

Google Cloud

Pros
  • Good ML tooling
  • TPU access
  • Preemptible discounts
Cons
  • Limited H100 availability
  • Complex quotas
Best For

ML teams using TensorFlow/JAX

Azure

Pros
  • Microsoft integration
  • OpenAI partnership
  • Enterprise support
Cons
  • High prices
  • Limited GPU availability
Best For

Microsoft shops, OpenAI API users

Lambda Labs

Pros
  • Simple pricing
  • Good availability
  • ML-focused
Cons
  • Smaller scale
  • US-only
Best For

Startups and researchers

Griddly

Recommended
Pros
  • Lowest prices
  • No commitments
  • Global network
  • Simple API
Cons
  • Newer platform
  • Best for batch workloads
Best For

Cost-conscious teams, batch training, inference

Our Recommendation

For most AI teams in 2025, we recommend a hybrid approach:

For Training

Use Griddly or similar DePIN networks for batch training jobs. The 50-70% savings compound quickly on multi-day training runs.

For Inference

Consider consumer GPUs (RTX 4090) for latency-tolerant inference. At $0.45/hr, they're unbeatable for cost-per-token.

For Production

Keep a small footprint on hyperscalers (AWS/GCP) for mission-critical, low-latency workloads where uptime guarantees matter.

Ready to Cut Your GPU Costs?

Start using Griddly today and save 50-70% on GPU compute. No commitments, no hidden fees, pay only for what you use.

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