Quick Answer: I switched from RunPod to VoltageGPU because:
- RTX 4090s are 49% cheaper ($0.18/hr vs $0.35/hr)
- A100s are 41% cheaper ($1.48/hr vs $2.49/hr)
- Per-second billing saved me $37 last month on short jobs
- Deploys take <60 seconds vs 3-5 minutes on RunPod
TL;DR: After 6 months and $2,300 spent on RunPod, I benchmarked VoltageGPU and saved 42% on average. Here’s the exact cost breakdown and migration steps.
The Cost Comparison
Live prices as of today (RunPod vs VoltageGPU):
| GPU | RunPod Price | VoltageGPU Price | Savings |
|---|---|---|---|
| RTX 4090 | $0.35/hr | $0.18/hr | 49% |
| A100 80GB | $2.49/hr | $1.48/hr | 41% |
| H100 80GB | $3.99/hr | $3.47/hr | 13% |
Note: H100 is actually 13% more expensive on RunPod, but VoltageGPU has limited availability (23 H100s live now).
What I Gained
- Per-second billing: My 7-minute fine-tuning jobs now cost $0.02 instead of $0.35 (RunPod’s 1-hour minimum)
-
Faster deploys:
curl-based API vs web UI clicks (example):
# Deploy an A100 in 30s
curl -X POST "https://api.voltagegpu.com/v1/pods" \
-H "Authorization: Bearer YOUR_KEY" \
-d '{"gpu_type":"a100", "name":"my-pod"}'
- OpenAI-compatible inference: Zero code changes when switching from RunPod’s custom API
What I Lost
- Fewer GPUs available: VoltageGPU had 155 H200s vs RunPod’s ~500 when I checked
- No community credits: RunPod gives free credits for social posts (but their $0.35/hr RTX 4090 still costs 2x more)
Migration Steps
- Export your RunPod data:
# RunPod’s CLI (install via pip)
runpod get pods --all > runpod_backup.json
- Deploy equivalent pods:
# VoltageGPU’s Python SDK
from voltagegpu import Pod
pod = Pod.create(gpu_type="a100", cloud="aws") # Or 'lambda', 'azure'
- Update inference endpoints: Just change the base URL if using OpenAI SDK:
openai.api_base = "https://api.voltagegpu.com/v1"
Why This Matters
For my 100-hour/month workload:
- RunPod RTX 4090: $35
- VoltageGPU RTX 4090: $18 That’s $204/year saved per GPU — enough to fund 1,133 extra inference hours.
Full docs: VoltageGPU API Reference
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