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Computer Science & Data / AI Infrastructure & LLM Economics
LLM Token & Cloud GPU Cost Estimator
All prices and throughput values are user inputs. Defaults are example assumptions, not market truth.
Optional GPU Estimate
Estimate your monthly LLM token usage and API/cloud GPU costs based on your traffic and token sizes.
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.
Worked Example
requestsPerSecond2
promptTokensPerRequest800
completionTokensPerRequest200
activeHoursPerDay12
activeDaysPerMonth30
apiPricePerThousandTokens0.015
gpuTokensPerSecond500
gpuPricePerHour3
Result: requestsPerMonth: 2592000; tokensPerMonth: 2592000000; apiCost: 38880; gpuHours: 1440; gpuCost: 4320At 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.
Frequently Asked Questions
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.