Local AI vs Cloud - Cost Calculator
Pick your hardware, your cloud provider, and your token volume. We'll tell you when (or if) the local rig pays for itself.
How this works
Cloud cost = (tokens/month ÷ 1M) × provider blended $/Mtok. Provider rates as of June 2026 (input + output blended 1:1).
Local cost = hardware MSRP ÷ 36 months + (TDP × load hrs + 15% idle × (24 − load hrs)) × 30 × $/kWh.
Supported cloud providers (8)
Frequently asked
When does a local AI GPU pay for itself vs OpenAI?
For sustained loads above ~2M tokens/day on GPT-4-class workloads, an RTX 4090 typically pays for itself in 6–12 months. For lighter loads or mini-model use cases, the cloud is often cheaper indefinitely.
Does electricity make local AI uneconomical?
Rarely in the US. A 450W GPU run 8 hours a day at $0.15/kWh is ~$16/month. Even at $0.40/kWh, it's ~$43/month - still less than scale cloud usage.
Why 36-month amortization?
Conservative middle-ground. Most GPUs hold residual value at 36 months and many serve 5+ years.
Caveats & limitations
- Doesn't model multi-user batching efficiency (cloud wins more for bursty workloads).
- Doesn't account for engineering time, monitoring, or cooling costs.
- Doesn't include fine-tuning credits or volume discounts on cloud APIs.
- Hardware amortization assumes straight-line 36 months; real resale varies.
- Cloud pricing is blended (1:1 input:output). Your actual ratio may differ.