Agentic AI Token Cost Estimator
Agentic AI token cost estimator
An AI agent doesn't make one LLM call — it loops, reasoning and calling tools over several steps, re-sending the growing context each time. So cost ≈ steps per task × (input tokens × input price + output tokens × output price). Enter your agent's shape below for cost per task, per day, and per month.
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Cost per task
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Cost per day
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Tokens per task
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input + output
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Book an assessment →Frequently asked questions
Why do AI agents cost more than a single LLM call?
They loop — reasoning, calling tools, reading results — over several calls per task, re-sending the growing context each time. Cost ≈ steps × tokens per step.
How do you estimate an agent's monthly cost?
Steps per task × (input tokens × input price + output tokens × output price) = cost/task, × tasks/day × days/month.
How can I reduce token costs?
Fewer steps, trim re-sent history and tool output, use a cheaper model for simple steps, and cache stable context. Controlling context size is the biggest lever.
Estimates only. Enter current published rates for your model — prices change and vary by tier, caching, and provider. Real agents vary in steps and context per task.