What is the difference between Secure Cloud and Community Cloud?

Secure Cloud runs in vetted Tier 3/4 data centers with redundant networking and is more reliable, so it costs more per hour. Community Cloud uses GPUs hosted by individual providers, which is cheaper but can vary in uptime and bandwidth. Use Secure Cloud for production APIs and Community Cloud for batch or experimental work.

Are these the exact prices RunPod charges?

The rates here are representative mid-2026 figures and are meant for planning, not billing. RunPod adjusts pricing by region and availability, so always confirm the live rate in the RunPod console before committing to a long run.

Does this include storage and network egress?

No. This estimates GPU compute time only. RunPod also bills for persistent network volumes and, on some tiers, data egress. Add those separately if your workflow stores large model files or transfers many output images.

How do I lower my per-job cost?

Reduce generation time by lowering step counts, using faster samplers, batching multiple images per job, and choosing a GPU that fits your VRAM needs without overpaying. A 4090 often beats an A100 on cost for SDXL because it finishes the job nearly as fast at a much lower hourly rate.

RunPod GPU Cost Calculator

RunPod GPU cost calculator

RunPod rents GPUs by the second across two tiers — Secure Cloud (data-center hosted, reliable) and Community Cloud (peer-hosted, cheaper). For AI image and video generation, the only number that matters for budgeting is how long each job holds the GPU multiplied by how often you run it. This calculator turns those two inputs plus a GPU choice into a clear monthly figure.

How it works

Pick a GPU and the tool loads its representative hourly rate for both cloud tiers. You enter the seconds per job (how long one generation takes on that hardware) and your jobs per month. The math is simple but easy to get wrong by hand: cost per job is hourlyRate / 3600 * secondsPerJob, and monthly cost is that figure times your volume. The result shows both tiers side by side so the reliability premium is explicit rather than buried in a console.

Tips for cutting cost

Match the GPU to the model, not the hype. An RTX 4090 finishes SDXL almost as fast as an A100 at a fraction of the hourly rate — for most image workloads it is the cheapest option per finished image.
Batch jobs. Generating four images in one container run amortizes startup overhead and keeps the GPU busy instead of idling between API calls.
Use Community Cloud for non-critical work. Batch upscaling, dataset generation, and experiments rarely need Secure Cloud uptime guarantees.
Remember storage. This figure is compute only. Persistent volumes holding large checkpoints add a small but real monthly cost on top.