Multi-Agent Cost & FinOps Simulator

Model the exact cost per completed task for agentic frameworks (LangGraph, CrewAI, AutoGen). Account for multi-turn tool calling, context snowballing, RAG vectors, and prompt caching savings.

Multi-step code synthesis, test execution loop, linter feedback, and PR generation.

⚙️ Workflow Architecture Parameters

Steps / Turns per Task: 8 Steps

Number of reasoning cycles, tool calls, and verification loops.

System Prompt & Tool Definitions: Tokens

Static system instructions, persona guidelines, and JSON schemas.

RAG / Vector DB Chunks Ingested: Tokens

Retrieved documentation and semantic search knowledge.

Tool Call / Environment Feedback (per step): Tokens

Terminal outputs, API responses, or database query results returned to agent.

Model Completion Output (per step): Tokens

Thought tokens, tool call arguments, and final synthesized text.

Monthly Task Executions: Tasks / Month

🧠 Step 1 Planner Model (High Reasoning):

⚡ Steps 2+ Executor Model (Fast Tool Caller):

Enable Prompt Caching on System Prompt & RAG

Cost per Resolved Task

$0.048

Unit economics per completed run

Monthly AI Bill

$240.00

Total infrastructure run cost

Total Tokens / Task

64,000 tokens

Accumulated in multi-turn context

Monthly Volume

320.0M Tokens

Combined input + output stream

💡 3 Production Cost Reduction Strategies

1. Model Cascade Routing: Use expensive frontier models (Claude 3.5 Sonnet, o1) only for initial intent triage and planning. Route subsequent tool execution steps to DeepSeek V3 or Gemini 2.0 Flash to save up to 85%.
2. Prefix Prompt Caching: Keep your system prompt and function schemas static at the beginning of the context. DeepSeek ($0.014/1M) and Anthropic ($0.30/1M) slash cached input rates by 90%.
3. Context Pruning / Summarization: After step 4, compress previous tool call JSON payloads into short natural language summaries before resending to avoid exponential quadratic token snowballing.