Agent Cost Calculator

Estimate the true cost of agentic workflows: multi-step LLM calls, tool execution, retries, task failures, and human review — per task and at production volume.

Workflow Configuration

LLM Model

Fast & cheap; good for structured agent steps · $0.4/1M in · $1.6/1M out

Workflow Steps (LLM Calls per Task)

Planning calls— Initial reasoning/CoT

Tool calls— Search, code exec, APIs

Validation calls— Self-check/reflection

Synthesis calls— Final answer generation

Total LLM calls/task: 6

Token Profile per Call

Input tokens / call— System prompt + history + context

Output tokens / call— Reasoning + response per step

Tool schema tokens / call— Tool definitions + result injected into context

Error & Review Rates

Retry rate: 15%— LLM calls retried due to bad output

0%50%

Task failure rate: 5%— Tasks that fail entirely (sunk cost)

0%30%

Human review rate: 10%— Tasks routed to a human

0%100%

Human review cost / task— Analyst / labour cost in USD

Production Volume

Daily task volume— Tasks completed per day

Configure your workflow and click Calculate

Cost breakdown by step will appear here

Agent Cost Optimization Tips

Use smaller models for tool-parsing steps. Route planning & synthesis to GPT-4.1 or Claude Sonnet, but use Haiku/Flash/nano for JSON extraction and validation steps — 5–20× cheaper.
Reduce tool schema tokens. Trimming tool descriptions from 600 to 200 tokens cuts input costs on every tool-call LLM call. Use concise tool definitions.
Cache repeated context. System prompts, tool schemas, and static RAG content repeated across calls are candidates for prompt caching (Anthropic, OpenAI both support this).
Track retry rate closely. A 20%+ retry rate usually signals prompt engineering issues — structured outputs (JSON mode, constrained decoding) often eliminate most retries.
Failure waste compounds fast. A 10% task failure rate on 1000 tasks/day means 100 tasks worth of LLM cost with zero business value. Add guardrails and early termination.

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