Best GPU Cloud Providers: Vast.ai vs RunPod vs TensorDock

If you’re looking to run ComfyUI, Stable Diffusion Forge, or Automatic1111 but don’t have a powerful GPU (or just want the flexibility of cloud computing), you’re probably wondering which GPU cloud provider is actually worth your money. Let me be honest: there are a lot of options out there, but three platforms stand out for AI image generation: Vast.ai, RunPod, and TensorDock.

I’ve tested all three, talked to other users, and compared pricing, features, and reliability. This guide will help you choose the right one for your specific needs — whether you’re on a tight budget, need the easiest setup, or want maximum control.

Why Use GPU Cloud Providers for ComfyUI & Forge?

Before we dive into the comparisons, let’s talk about why you’d want to use a cloud GPU in the first place. Here’s the reality: high-end GPUs are expensive. A single RTX 4090 costs over $1,500, and that’s if you can even find one at MSRP. If you want an A100 or H100 for serious work? Good luck — those are enterprise-grade cards that cost thousands.

GPU cloud providers let you:

• ✅ Pay only when you use it — perfect if you’re not generating 24/7
• ✅ Access high-end GPUs (A100, H100, RTX 4090) without buying hardware
• ✅ Scale up or down based on your project needs
• ✅ Avoid local setup headaches — most providers have one-click templates

The catch? You need to manage your instances carefully, or you’ll end up with surprise bills. But with the right provider, it’s actually a pretty sweet deal.

Quick Comparison Table

Here’s a snapshot of how these three providers stack up:

🏆 Vast.ai: The Budget Champion

Website: vast.ai

Vast.ai is a decentralized GPU marketplace — think of it as the “Airbnb of GPUs.” Individual providers and data centers list their GPUs, and you rent them. This means you get a huge selection of hardware at some of the lowest prices you’ll find anywhere.

✅ Pros of Vast.ai

• Incredibly Affordable — Seriously, this is where Vast.ai shines. RTX 4090 instances start around $0.31/hour, which is often 30–50% cheaper than competitors. If you’re on a tight budget, this is hard to beat.
• Massive GPU Selection — Because it’s a marketplace, you have access to everything from consumer RTX cards to enterprise A100s and H100s. Someone’s always listing something.
• Flexibility — You can run pretty much any software, including custom Docker images. Want to tweak your ComfyUI setup? Vast.ai doesn’t care — it’s your VM.
• Pay-Per-Second Billing — You only pay for what you use, down to the second. No monthly commitments.

❌ Cons of Vast.ai

• Less Beginner-Friendly — The interface isn’t as polished as RunPod, and you’ll need to know your way around Docker images. If you’re not comfortable with technical setup, you might struggle.
• Inconsistent Providers — Since it’s a marketplace, provider quality varies. Some hosts are excellent; others might have connectivity issues or slower drives. You’ll want to check reviews.
• Manual Monitoring Required — You need to actively manage your instances and shut them down when done, or you’ll rack up charges. Set up billing alerts.
• No Pre-built AI Templates — Unlike RunPod, you’ll mostly be setting things up yourself or finding community Docker images.

💰 Pricing Examples

• RTX 4090: ~$0.31/hour
• RTX 3090: ~$0.22/hour
• A100 (40GB): ~$1.00/hour
• H100: ~$1.35/hour

Who Should Use Vast.ai?

Vast.ai is perfect if you:

• Have a tight budget and need maximum GPU power for your dollar
• Are comfortable with Docker and technical setup
• Don’t mind a bit of trial and error to find good providers
• Want maximum flexibility to run any software

🚀 RunPod: The User-Friendly Favorite

Website: runpod.io

RunPod is built specifically for AI and machine learning workloads. It’s designed to make deploying ComfyUI, Forge, and other AI tools as easy as possible — and honestly, they’ve done a pretty good job at it.

✅ Pros of RunPod

• One-Click Templates — RunPod offers over 50 pre-built templates for popular AI frameworks, including ComfyUI, Automatic1111, and Forge. You can literally have ComfyUI running in under 5 minutes.
• Great for Beginners — The interface is clean, intuitive, and well-documented. If you’re new to cloud GPUs, RunPod is probably your safest bet.
• Persistent Storage — You can mount persistent storage volumes, which is great for saving models, workflows, and outputs. No need to re-download everything each time.
• Community & Secure Cloud Options — Choose between cheaper community instances (shared resources) or dedicated secure cloud GPUs. The community option is perfect for testing.
• Active Community — RunPod has a solid Discord community and good documentation, so help is easy to find.

❌ Cons of RunPod

• Higher Pricing — RunPod is more expensive than Vast.ai, typically 20–30% higher. An RTX 4090 runs around $0.32/hour (though prices fluctuate).
• Community Instances Can Be Preempted — If you’re using the cheaper community cloud, your instance might get terminated if someone else needs the GPU. It’s fine for testing, but annoying for long runs.
• Template Reliability — Some templates can be outdated or buggy. You might still need to do some setup yourself, despite the “one-click” promise.
• Limited GPU Selection — While they have good coverage, Vast.ai’s marketplace model gives you more options.

💰 Pricing Examples

• RTX 4090: ~$0.32/hour (Community: ~$0.29/hour)
• RTX 3090: ~$0.24/hour
• A100 (40GB): ~$0.89/hour
• H100: ~$1.89/hour

Who Should Use RunPod?

RunPod is perfect if you:

• Want the easiest setup possible — especially for ComfyUI
• Are new to cloud GPUs and want good documentation
• Value convenience over absolute lowest price
• Need reliable, consistent performance

⚙️ TensorDock: The Power User’s Choice

Website: tensordock.com

TensorDock is another marketplace-style platform, but with a focus on full virtual machine control and KVM isolation. It’s somewhere between Vast.ai’s flexibility and RunPod’s polish.

✅ Pros of TensorDock

• Full VM Control — You get a complete virtual machine with KVM isolation, which means better security and full OS functionality. If you need to install custom software or modify system-level settings, TensorDock gives you that control.
• Customizable Resources — You can select CPU, RAM, and storage separately, so you’re only paying for what you actually need. This is great if you have specific requirements.
• Competitive Pricing — TensorDock often beats RunPod on pricing (sometimes by 20–30%), especially for certain GPU models. They claim up to 30% savings over competitors.
• Good Security — KVM isolation means your VM is fully isolated from other users, which is important for production workloads.
• Customizable Templates — While not as extensive as RunPod, they do offer templates and you can create your own.

❌ Cons of TensorDock

• Uptime Variability — Like Vast.ai, TensorDock uses a mix of consumer and data center GPUs from various providers, which can mean spotty uptime or performance inconsistencies. Read provider reviews.
• Less Polished Interface — The UI isn’t as refined as RunPod, though it’s better than Vast.ai. There’s a learning curve.
• Limited Additional Services — They don’t offer things like object storage buckets or some of the extra features that RunPod provides.
• Smaller Community — The user base is smaller than RunPod, so finding help or community resources can be trickier.

💰 Pricing Examples

• RTX 4090: ~$0.40/hour
• RTX 3090: ~$0.28/hour
• A100 (40GB): ~$2.25/hour
• H100: ~$2.69/hour

Who Should Use TensorDock?

TensorDock is perfect if you:

• Need full VM control and system-level customization
• Want better security through KVM isolation
• Have specific CPU/RAM/storage requirements
• Don’t mind a bit more setup complexity for better control

Which Provider Should You Choose?

Here’s my honest take on who should pick what:

Choose Vast.ai if:

• 💰 Budget is your #1 concern — You need the cheapest GPUs possible
• 🛠️ You’re technical — Comfortable with Docker, VMs, and troubleshooting
• 🔧 You want maximum flexibility — Need to run custom setups

Choose RunPod if:

• 🎯 Easy setup matters most — You want ComfyUI running in minutes
• 📚 You’re learning — Good documentation and community support
• ⚡ You want reliability — Less variability than marketplace providers

Choose TensorDock if:

• 🔒 You need security/isolation — Production workloads, sensitive data
• 🎛️ You need custom resources — Specific CPU/RAM/storage combos
• 💼 Professional use — Full VM control for enterprise needs

Real-World Usage Tips

No matter which provider you choose, here are some tips that’ll save you money and headaches:

💡 Cost Management

• Set billing alerts — Most providers let you set spending limits. Do it. Trust me.
• Shut down instances when done — Sounds obvious, but it’s easy to forget and rack up $50+ in charges overnight.
• Use spot/community instances for testing — Save the dedicated GPUs for actual work.
• Check prices regularly — GPU prices fluctuate based on demand. Sometimes you can save 30% by waiting a few hours.

⚙️ Setup Best Practices

• Start with templates — Even on Vast.ai, look for community Docker images first before building your own.
• Use persistent storage — Don’t download models every time. Mount storage volumes.
• Keep your setup documented — You’ll forget what you did last month. Write it down.
• Test before committing — Spin up a cheap instance first, get everything working, then scale up.

🔍 Finding Good Providers (Vast.ai & TensorDock)

• Check reviews/ratings — Both platforms show provider ratings. Stick to 4+ stars.
• Test connectivity — Ping times matter. Check before committing to long runs.
• Verify GPU models — Make sure you’re actually getting the GPU you paid for.
• Start small — Try a short rental first before committing to a long session.

Pricing Comparison (2025 Update)

Here’s a current snapshot of hourly rates for common GPUs:

Note: Prices are approximate and fluctuate based on demand, location, and provider. Always check current rates on each platform. Community/shared instances are typically 10–20% cheaper.

Setting Up ComfyUI on Each Platform

Here’s a quick rundown of what setup looks like on each platform:

Vast.ai Setup

1. Search for an RTX 4090 or A100 instance
2. Choose a Docker image (look for “comfyui” in community images)
3. Configure ports (usually 8188 for ComfyUI)
4. Deploy and connect via SSH/VNC
5. Time to first image: 15–30 minutes (depending on your Docker skills)

RunPod Setup

1. Go to Templates → Search “ComfyUI”
2. Click “Deploy” on a template
3. Wait for instance to spin up (~2–3 minutes)
4. Access via provided URL
5. Time to first image: 5–10 minutes

TensorDock Setup

1. Choose GPU and create instance
2. Select a template or custom image
3. Configure networking and storage
4. Deploy and connect
5. Time to first image: 10–20 minutes

Related Guides

• FLUX in ComfyUI: /blog/flux-comfyui-guide
• FLUX in Stable Diffusion Forge: /blog/flux-forge-guide
• SDXL Best Practices: /blog/sdxl-best-practices-guide
• Stable Diffusion Prompting: /blog/stable-diffusion-prompting-guide

✅ Final Thoughts

Choosing a GPU cloud provider really comes down to three things: budget, ease of use, and flexibility.

• Vast.ai gives you the lowest prices and most flexibility, but you’ll work for it.
• RunPod gives you the easiest experience, especially for ComfyUI, but you’ll pay a bit more.
• TensorDock gives you full control and security, but it’s more complex.

My recommendation? Start with RunPod if you’re new — the templates and documentation will get you up and running fast, and you can always switch later. If you’re on a tight budget and technically comfortable, try Vast.ai — you’ll save serious money. For production work or when you need specific configurations, TensorDock is worth considering.

The good news? All three platforms offer trial credits or have affordable community tiers, so you can test them out without breaking the bank. Try a few, see which one fits your workflow, and remember to shut down your instances when you’re done — your wallet will thank you.

📚 Additional Resources

• Vast.ai Official Site - GPU marketplace
• RunPod Official Site - AI-focused cloud platform
• TensorDock Official Site - VM-based GPU cloud
• ComfyUI GitHub - Official ComfyUI repository
• Stable Diffusion Forge GitHub - Forge project

Last updated: January 2025. Pricing and features subject to change.