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You need GPUs for AI. The question is where to get them. The answer depends on whether you’re doing inference (serving a model) or training (fine-tuning), how much you’re spending, and how much ops work you want to do.
The providers
Tier 1: GPU-first clouds (best price/performance)
RunPod
- A100 80GB: ~$1.64/hr (community) to $2.49/hr (secure)
- H100 80GB: ~$3.29/hr
- Serverless GPU option (pay per second)
- Best for: inference serving, batch jobs, experimentation
- runpod.io
Lambda
- A100 80GB: $1.99/hr
- H100 80GB: $2.49/hr
- On-demand and reserved instances
- Best for: training, long-running jobs
- lambda.ai
Tier 2: General clouds with GPU options
DigitalOcean
- GPU Droplets with NVIDIA H100
- Simpler UX than hyperscalers
- Best for: teams already on DigitalOcean, simpler GPU needs
- digitalocean.com
Vultr
- A100 and H100 instances
- Global locations, competitive pricing
- Best for: inference at scale, geographic distribution
- vultr.com
- Dedicated Intel Xeon / AMD EPYC servers
- 100% hardware access, no virtualization overhead
- Best for: heavy inference workloads needing full hardware control
Tier 3: Hyperscalers (most features, highest price)
AWS (EC2 P5/P4)
- Most GPU options, best availability
- Most expensive, most complex
- Best for: enterprise, existing AWS infrastructure
Google Cloud (A3/A2)
- TPU option for training
- Good Vertex AI integration
- Best for: teams using Google ecosystem
Azure (NC/ND series)
- Good for enterprise with Microsoft agreements
- Best for: teams using Azure/OpenAI
Pricing comparison (as of April 2026)
| GPU | RunPod | Lambda | Vultr | AWS | GCP |
|---|---|---|---|---|---|
| A100 80GB | $1.64-2.49/hr | $1.99/hr | ~$2.06/hr | ~$3.67/hr | ~$3.67/hr |
| H100 80GB | $3.29/hr | $2.49/hr | ~$3.50/hr | ~$4.50/hr | ~$4.50/hr |
| Monthly (24/7) | $1,180-2,370 | $1,430-1,790 | $1,480-2,520 | $2,640-3,240 | $2,640-3,240 |
GPU-first clouds are 30-50% cheaper than hyperscalers for the same hardware.
Which to pick
For inference (serving models to users)
Use RunPod Serverless if your traffic is bursty. You pay per second of GPU time, and it scales to zero when idle. Perfect for vLLM serving with variable load.
Use Vultr, Contabo or DigitalOcean if you need always-on inference with predictable traffic.
For training / fine-tuning
Use Lambda for the best H100 pricing on reserved instances. Their software stack is optimized for training workloads.
Use RunPod for shorter training runs where you don’t want to commit to reserved capacity.
For teams already on a hyperscaler
Stay where you are. The 30-50% savings from GPU-first clouds isn’t worth the operational complexity of managing another provider if your data and pipelines are already on AWS/GCP/Azure.
The self-hosted alternative
For predictable, high-volume inference, self-hosting on dedicated hardware is cheapest long-term:
| Setup | Monthly cost | Break-even vs cloud |
|---|---|---|
| RTX 4090 workstation | ~$105 (amortized) | 2-3 months |
| Mac Mini M4 | ~$50 (amortized) | 1-2 months |
| A100 server (rented) | ~$500 | 4-6 months vs hyperscaler |
See our GPU memory planning guide for sizing and our inference cost calculator for break-even analysis.
Availability: the real bottleneck
Pricing means nothing if you can’t get a GPU. Availability varies wildly:
| Provider | H100 availability | A100 availability |
|---|---|---|
| RunPod | Usually available (community cloud) | Good |
| Lambda | Often waitlisted for on-demand | Good for reserved |
| DigitalOcean | Limited regions | Good |
| AWS | Spot instances available, on-demand waitlisted | Good |
Tips for getting GPUs:
- Use spot/preemptible instances for batch jobs (50-70% cheaper, can be interrupted)
- Reserve capacity if you need guaranteed availability (commit for 1-3 months)
- Multi-provider strategy — have accounts on 2-3 providers so you can switch when one is out of stock
- Off-peak hours — GPU availability is better during US nighttime
Decision framework
Budget < $100/mo? → Self-host on Mac/consumer GPU
Budget $100-500/mo? → RunPod serverless or Vultr
Budget $500-2000/mo? → Lambda reserved or RunPod dedicated
Budget > $2000/mo? → Negotiate reserved pricing with any provider
Already on AWS/GCP? → Use their GPU instances
Need guaranteed SLA? → Hyperscaler reserved instancesFAQ
What’s the best cloud GPU provider in 2026?
RunPod is the best for on-demand GPU workloads with pay-per-second billing and fast spin-up times. For production workloads with steady traffic, Vultr offers predictable monthly pricing. Your choice depends on whether you need burst or sustained compute.
How much do cloud GPUs cost?
Prices range from $0.50/hour for older GPUs (A10G) to $3-4/hour for A100 80GB instances. Serverless options like RunPod charge per-second, so short inference jobs can cost pennies. Reserved instances offer 30-60% discounts for committed usage.
Should I use cloud GPUs or buy my own?
If you need GPUs less than 6-8 hours per day, cloud is cheaper. If you’re running inference 24/7, buying hardware (or a dedicated server) pays for itself within 3-6 months. Most developers start with cloud and migrate to owned hardware as usage grows.
Related: How to Serve LLMs with vLLM · GPU Memory Planning · Self-Hosted AI for Enterprise · Serverless vs Dedicated GPU · Best Hosting for AI Side Projects
⚡ Best for on-demand GPU: RunPod — pay-per-second serverless GPUs, no commitment. Spin up an A100 in seconds, shut it down when you’re done. Sign up through our link and get $5 in free GPU credits to start.
Best for always-on inference: Vultr — dedicated GPU instances with predictable monthly pricing. Better for production workloads with steady traffic.