How much can you cut from your LLM API bill?
Just enter your current model and monthly spend — we'll automatically compare the best self-hosted GPU setup and alternative APIs.
See monthly, annual, and 5-year TCO, savings rate, and migration risk — for both self-hosting and API options.
About this LLM API cost optimization tool
This tool estimates how much you could save by self-hosting on GPUs or switching to another API model, based only on your current LLM API cost.
Rather than simply recommending the cheapest model, we propose realistic alternatives within the same performance tier as your current model, or at most one tier below.
All figures shown are estimates, not measured values based on your actual billed tokens or benchmarks. We recommend PoC validation before adoption.
Model performance index source artificialanalysis.ai · gpucost.org
Frequently Asked Questions
Can token usage be estimated from API cost alone?
Yes — we reverse-calculate monthly tokens from the current model's official unit price and a default input:output ratio. This is an estimate; entering actual billed token counts improves accuracy.
Is self-hosted GPU or an LLM API cheaper?
It depends on scale. At small scale, an API is usually more favorable; at large, sustained scale, self-hosting can win on 5-year TCO. This tool estimates both so you can compare.
How many B200 GPUs do I need?
It depends on your monthly token volume, peak multiplier, and the performance tier of your selected model. This tool automatically calculates the required GPU count from your average and peak throughput.
How is 5-year depreciation calculated?
Initial investment (CAPEX) is depreciated straight-line over 60 months, plus monthly power, maintenance, staffing, software, and API-fallback costs.
Can I compare MiniMax and Gemini costs?
Yes. Using both models' official pricing data, monthly cost is compared under the same assumed usage.
Is real-world performance actually the same?
No, this cannot be guaranteed. The displayed performance tier and token speed are estimates based on benchmarks and specs. We recommend PoC validation before production use.