GPU Cloud Pricing Comparison 2024
GPU cloud pricing is confusing. Between on-demand, spot, reserved, and committed use discounts, it's hard to know what you'll actually pay. Here's a no-BS comparison of what GPU inference actually costs in 2024.
TL;DR
- →Vectorlay: $0.29-0.49/hr for consumer GPUs with auto-failover
- →Hyperscalers (AWS/GCP/Azure): $2-4+/hr, enterprise SLAs, complex pricing
- →GPU Clouds (RunPod, Lambda): $0.74-2.49/hr, data center grade
- →Serverless (Modal, Replicate): Per-second billing, higher rates, cold starts
The GPU Pricing Problem
Let's be honest: cloud GPU pricing is a mess. Providers use different GPU models, different pricing structures, and different terminology. Some charge per hour, some per second. Some have minimum commitments, some don't.
And the sticker price? That's just the beginning. Add in storage, networking, egress fees, and suddenly your $2/hour GPU costs $3.50/hour.
This guide cuts through the noise with real, comparable prices for running ML inference workloads.
The Big Picture: Price Per GPU-Hour
Here's what you'll pay for a high-end GPU (24GB VRAM, comparable to RTX 4090 performance) across major providers:
| Provider | GPU | $/hour | $/month* |
|---|---|---|---|
| Vectorlay | RTX 4090 (24GB) | $0.49 | $353 |
| Vectorlay | RTX 3090 (24GB) | $0.29 | $209 |
| RunPod | RTX 4090 (24GB) | $0.74 | $533 |
| Lambda Labs | A10 (24GB) | $0.75 | $540 |
| Vast.ai | RTX 4090 (24GB) | $0.40-0.80 | $288-576 |
| AWS | A10G (24GB) | $1.21 | $871 |
| AWS | A100 (40GB) | $3.67 | $2,642 |
| GCP | A100 (40GB) | $3.67 | $2,642 |
| Azure | A100 (40GB) | $3.40 | $2,448 |
| CoreWeave | A100 (40GB) | $2.21 | $1,591 |
* Monthly cost assumes 24/7 usage (720 hours). Prices as of December 2024. On-demand pricing shown; reserved/committed pricing may be lower.
How Much Can You Save?
Let's do the math for a typical inference workload:
Scenario: Running an LLM 24/7
You're running Llama 2 70B for a chatbot. Needs 2x 24GB GPUs.
Provider Categories Explained
Hyperscalers (AWS, GCP, Azure)
Best for: Fortune 500 companies with compliance requirements and existing AWS/GCP/Azure contracts.
GPU Cloud Providers (Lambda Labs, CoreWeave)
Best for: ML teams doing both training and inference who want a simpler experience than hyperscalers.
Marketplace Providers (Vast.ai, RunPod)
Best for: Hobbyists, researchers, cost-sensitive development workloads.
Serverless GPU (Modal, Replicate, Banana)
Best for: Bursty workloads, prototypes, low-volume production where cold starts are acceptable.
Vectorlay: Distributed GPU Network
Best for: Startups, indie hackers, and teams running inference that need reliability without enterprise prices.
Decision Matrix: What Should You Use?
| Use Case | Best Option | Why |
|---|---|---|
| 24/7 inference, cost-sensitive | Vectorlay | Lowest cost with built-in reliability |
| Bursty traffic, scale to zero | Modal / Replicate | Pay only for active compute |
| Enterprise, compliance needed | AWS / GCP / Azure | SLAs, SOC2, HIPAA, etc. |
| Training + inference combined | Lambda Labs / CoreWeave | H100s, NVLink, high-bandwidth storage |
| Experimentation, development | RunPod / Vast.ai | Cheap, flexible, good for testing |
| Startup production workloads | Vectorlay | Production-ready reliability at dev prices |
Hidden Costs to Watch For
The GPU price is rarely the full story. Watch for:
Egress Fees
AWS charges ~$0.09/GB for data leaving their network. If you're serving images or audio, this adds up fast. Vectorlay: No egress fees.
Storage Costs
Model weights need storage. On hyperscalers, that's $0.08-0.12/GB/month for fast SSD. Vectorlay: Storage included.
Minimum Commitments
Some providers require 1-hour minimums or monthly commitments. Vectorlay: Per-minute billing, no minimums.
Load Balancer Costs
AWS ELB costs $0.02/hour + $0.008/GB processed. GCP similar. Vectorlay: Load balancing included.
A Note on Performance
"But is an RTX 4090 as good as an A100?"
For inference? Often yes. The RTX 4090 has:
- →24GB GDDR6X VRAM (vs 40GB HBM on A100)
- →83 TFLOPS FP32 (vs 19.5 TFLOPS on A100)
- →Ada Lovelace architecture with better power efficiency
For models that fit in 24GB (most 7B-34B LLMs, Stable Diffusion, Whisper), the 4090 often outperforms the A100 on inference throughput while costing 1/7th the price.
A100s shine for training (HBM bandwidth, NVLink) and huge models (70B+ parameters). For inference? Consumer GPUs are often the smarter choice.
The Bottom Line
Cloud GPU pricing is designed to be confusing. Providers benefit when you can't easily compare prices.
Here's the simple truth:
- →If you need enterprise compliance → AWS/GCP/Azure
- →If you need massive scale training → Lambda/CoreWeave
- →If you need cheap, reliable inference → Vectorlay
We built Vectorlay for the 90% of use cases where you don't need a $2,600/month A100—you need a $350/month GPU that just works.
See the savings for yourself
Deploy your first cluster free. No credit card required. No hidden fees. Just cheap, reliable GPU inference.
Prices accurate as of December 2024. Cloud pricing changes frequently—always verify current rates on provider websites. This comparison uses on-demand pricing; reserved instances and committed use discounts may lower costs on some platforms.