Cloud GPU vs Local Hardware Calculator

A: Choose cloud GPUs if: you use AI less than 100 hours/month, need different GPU types, want zero maintenance, or are just starting out. Buy local hardware if: you use AI 200+ hours/month, need consistent access, want privacy, or plan long-term projects. Our calculator shows your exact break-even point.

A: Yes! Cloud providers offer the same RTX 4090, A100, and H100 GPUs you can buy locally. Sometimes even better since you get access to enterprise-grade hardware with better cooling and maintenance. The main difference is network latency (1-5ms) which is negligible for most AI tasks.

A: Besides the GPU cost, consider: electricity ($15-50/month), maintenance (fans wear out), replacement cycles (GPUs become obsolete in 2-3 years), opportunity cost of capital, and time spent troubleshooting. Cloud providers include all maintenance and upgrades.

A: Absolutely! Most cloud providers let you export your work and switch with minimal downtime. You can even run on multiple providers simultaneously for redundancy. Compare RunPod (gaming GPUs), Vast.ai (cheapest), Lambda Labs (professional), and Paperspace (easy setup).

A: Very reliable! Major providers like RunPod and Vast.ai have 99.9% uptime with automatic failover. Your data is backed up automatically, and if a GPU fails, they instantly move you to another one. This is actually more reliable than a single home setup.

A: Cloud providers take security seriously with encryption, isolated environments, and compliance certifications. However, if you're working with highly sensitive data, local hardware gives you complete control. Most AI workloads (training, inference) are fine with cloud security.

A: Use spot instances (50-80% cheaper), choose the right GPU size, stop instances when not using, use auto-scaling for batch jobs, and take advantage of provider promotions. Our calculator helps you find the optimal provider for your usage pattern.

A: Yes! Cloud makes it easy to spin up multiple instances or use larger GPUs with more VRAM. You can run Llama 70B, Stable Diffusion, and other models simultaneously, something that would require thousands in hardware investment locally.

A: With cloud, you can instantly upgrade to A100 or H100 GPUs for demanding tasks. No hardware limitations. You can also use multiple GPUs in parallel for distributed training, which would cost $20,000+ to set up locally.

A: Cloud GPU costs are typically fully tax-deductible as business expenses. Hardware purchases may need to be depreciated over several years. Consult your tax advisor, but cloud often offers better immediate tax benefits for businesses.