When Does Renting a GPU Beat Paying Per Token?
Enter your monthly token volume and the unit economics of both paths. The calculator finds the exact crossover where a self-hosted LLM endpoint costs less than a managed API.
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How the break-even is computed
The API path scales linearly with traffic, while the self-host path is dominated by a fixed monthly compute floor that barely moves as volume grows. The crossover is where those two lines meet.
self_cost = gpu_rate × gpus × 730 × utilization + ops_overhead
blended_api_rate = api_cost / (in_M + out_M)
break_even_tokens (M) = self_cost / blended_api_rate
Here in_M and out_M are millions of input and output tokens, and 730 is the average number of hours in a calendar month. Utilization scales the GPU hours: a node that only serves traffic 40% of the day at util = 40% still bills for the rest unless you tear it down, so leaving it at 100% reflects an always-on reserved instance.
Because the API blends two prices (cheap input, expensive output), the calculator derives a single blended rate from your actual input-to-output mix rather than assuming a symmetric workload. That matters: a RAG pipeline that stuffs long retrieved contexts into the prompt is input-heavy and breaks even much later than a generation-heavy chatbot, even at identical total token counts. The break-even line therefore shifts with your prompt shape, not just your volume.
The self-host floor also captures the hidden cost most spreadsheets miss — the engineer-hours to patch, monitor, and keep an inference server (vLLM, TGI, or similar) healthy. Folding that into ops_overhead raises the floor and pushes break-even higher, which is why a single hobby GPU rarely beats an API until you are comfortably into hundreds of millions of tokens per month. Below the crossover, the API wins on pure economics and on operational simplicity; above it, owning throughput compounds in your favor, and any spare GPU capacity can be reused for embeddings or fine-tuning at near-zero marginal cost.