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AI Agent Workforce Cost Calculator

Estimate monthly cost for multi-agent orchestration: planners, workers, reviewers, tool users, shared memory, handoffs, and human review.

Scenario presets

Workforce configuration

LLM provider Tasks per month Coordination rounds Cached input (%) Planner / Supervisor count Worker Agent count Reviewer / Critic count Tool / API Caller count Memory / RAG Agent count Tool preset Memory / RAG preset Human review rate (%) Review minutes / task Reviewer hourly cost ($) Setup hours Setup hourly rate ($) Maintenance hours / month Maintenance hourly rate ($) Projection (months)

Estimates are directional. Last updated: 2026-07-08. See notes.

Agent calls / month

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LLM orchestration cost

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Tool + memory cost

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Total monthly cost

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Cost per task

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Handoff cost / month

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Human review cost / month

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Setup cost (one-time)

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Projected period cost

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Avg agents per task

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Monthly cost breakdown

Cost line	Monthly	Share
Agent LLM calls	—	—
Handoff / coordination	—	—
Tool / API calls	—	—
Shared memory / RAG	—	—
Human review labor	—	—
Maintenance labor	—	—
Total monthly	—	—

Verdict

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Frequently asked questions

What does this calculator estimate?

It estimates the all-in monthly cost of a multi-agent workforce: orchestration LLM calls for each agent role, handoff messages between agents, shared memory/RAG queries, external tool/API calls, and human review labor.

How are handoff costs counted?

Each coordination round adds handoff tokens per active agent. These represent summaries, context passing, and supervisor instructions exchanged between agents.

What is a coordination round?

A round is one cycle where agents pass outputs to a planner/supervisor or to each other. A simple pipeline has 1 round; an iterative reflection loop may have 3+.

Why separate tool-user and memory agents?

Some agents call external APIs (search, browser, code interpreters) while others manage shared context or retrieval. Separating them lets you size those cost buckets independently.

Which is usually the biggest cost driver?

Often the worker agents, because there are more of them and they produce the longest outputs. Large context windows in planner or memory agents can also dominate if not cached.

How do I reduce multi-agent cost?

Use cheaper models for routing and summarization, cache repeated context, limit coordination rounds, batch tool calls, and downsample human review where model confidence is high.

API Pricing Comparator → Memory / RAG Cost Calc → Tool Call Cost Calc → Agent Stack Builder →

Prices are approximate per-token list rates as of mid-2026. Managed orchestration platforms and frameworks may charge by task, agent, or run rather than by token; treat results as directional for budgeting.