AI Agent Cost Calculator

Plug in your expected token volume, model choice, and run frequency to see what an AI agent will actually cost per month. The estimator multiplies input/output token counts by current per-million-token rates, adds tool-call overhead, and shows a monthly burn figure plus a per-task cost.

Use it before you commit to a model. Switching from a frontier model to a mid-tier one often cuts costs by 70% with minimal quality drop on routine tasks. The calculator surfaces that gap so you can pick the cheapest model that still works.

Treat the numbers as ballpark — vendor pricing changes often, and real workloads have caching, retries, and failed runs that no calculator captures cleanly.

The math behind it: cost_per_run = (input_tokens × input_rate + output_tokens × output_rate) / 1,000,000, then multiplied by runs_per_day and 30 for the monthly figure. Output tokens are usually 3-5x more expensive than input tokens — a model priced at $3/M input and $15/M output charges you five times more per word generated than per word read. Tilt your prompts toward giving the model context (cheap input) and asking for terse output (expensive). A 500-word system prompt that prevents 200 words of meandering output saves money on every run.

Worked example: pick GPT-4o-class pricing at $2.50/M input and $10/M output. Your agent reads ~3,000 input tokens (system prompt + user message + retrieved context) and writes ~500 output tokens per run. Math: (3,000 × 2.50 + 500 × 10) / 1,000,000 = $0.0125 per run. At 200 runs/day that's $2.50/day, or $75/month. Switch to a Haiku-class model at $0.25/M input and $1.25/M output and the same workload drops to $7.50/month — a 90% cut.

Where the estimate misses: tool-calling roundtrips multiply token counts (each tool result rides back into the next turn as input), agentic retries on bad output double or triple the spend, and conversation history grows linearly with turn count if you don't trim it. Add a 30-50% buffer on top of the calculator's number for production systems with any real traffic. When the agent has to read a 50-page PDF on every run, you're paying for those tokens every single time unless you cache them.