AI Agent Inference Capacity Planner
Size GPU or API capacity for an AI agent workload. Target requests/minute, latency, and cost across cloud rental, managed APIs, and self-hosted stacks.
Workload
Estimates are directional in USD. Last updated: 2026-07-08. See notes.
Required capacity
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GPU / cloud sizing
Monthly cost comparison
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Frequently asked questions
How do I convert requests per minute into required GPU capacity?
First estimate the tokens per request and the model's throughput in tok/s. Each request needs (input + output) tokens. Divide by tok/s to get the minimum wall-clock time per request, then add concurrency and utilization buffers so the GPU is not pegged at 100%.
What is a safe utilization target?
70-75% is typical for steady-state production. Lower if your traffic is bursty or if you need headroom for failovers. Higher is fine for batch or offline workloads.
Should I count input tokens or only output tokens?
Both. The model must process the full prompt (input) before generating output. Input tokens often dominate cost and latency, especially with long system prompts and RAG context.
When is a managed API cheaper than renting GPUs?
Managed APIs win at low or intermittent volume because you pay only for tokens used. Renting GPUs wins when utilization is high and you can keep the machine busy. This calculator compares both so you can find the crossover.
Does this account for batching?
The GPU tok/s estimates assume a single request stream. In practice, dynamic batching and continuous batching can raise effective throughput, but latency also increases. Use the concurrency buffer and utilization target to approximate real-world headroom.
Throughput, latency, and price estimates are directional. Real performance depends on model implementation, batching, quantization, network overhead, and workload burstiness. Confirm provider benchmarks before buying capacity.