LLM Token & Cloud GPU Cost Estimator
Optional GPU Estimate
About This Calculator
This llm token & cloud gpu cost estimator helps you move from raw inputs to a decision-ready output in seconds.
Estimate your monthly LLM token usage and API/cloud GPU costs based on your traffic and token sizes.
If your workflow expands, pair this calculator with AI Infrastructure Total Cost of Ownership (On-Prem vs Cloud GPU) Calculator and LLM Vendor Cost Comparison – API vs Self-Hosted to cross-check assumptions and build a stronger analysis chain.
Formula
R_m = r * 3600 * h_d * d_m; T = T_p + T_c; tokens_m = R_m * T; C_api = (tokens_m / 1000) * p_api; optional: GPUhours = tokens_m / (q * 3600); C_gpu = GPUhours * p_gpu.
Example Calculation
The worked example below demonstrates how the input fields translate into the final output. Use it as a quick validation pass before entering your own numbers.
- requestsPerSecond: 2
- promptTokensPerRequest: 800
- completionTokensPerRequest: 200
- activeHoursPerDay: 12
- activeDaysPerMonth: 30
- apiPricePerThousandTokens: 0.015
- gpuTokensPerSecond: 500
- gpuPricePerHour: 3
Explanation of Results
Result Interpretation
At 2 RPS with 1,000 tokens per request, you use about 2.6B tokens per month. At 1.5 cents per 1,000 tokens, this is roughly 38.9k in API spend; a GPU cluster delivering 500 tokens/sec per GPU at 3 dollars/hour would cost about 4.3k for the same workload.
FAQ
What if my traffic is highly spiky?
Estimate multiple traffic scenarios and compare low, average, and peak assumptions so monthly cost expectations include variability.
How accurate are the GPU throughput assumptions?
Treat throughput as a user-controlled assumption and calibrate it with your own benchmark data for the model and serving stack you plan to run.
Can I use this for multiple models at once?
Yes. Run one scenario per model or combine traffic into weighted assumptions if you want a single blended estimate.
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See Also
Other calculators in AI Infrastructure & LLM Economics