LLM Usage Tracker & Cost Monitor with Node-Level Analytics (v2)

Description

LLM Cost Monitor & Usage Tracker for n8n

> v2: Now it can read multiple types of LLM usages. Better dynamic approach for reading model usage.

🎯 What This Workflow Does

This workflow provides comprehensive monitoring and cost tracking for all LLM/AI agent usage across your n8n workflows. It extracts detailed token usage data from any workflow execution and calculates precise costs based on current model pricing.

The Problem It Solves
When running LLM nodes in n8n workflows, the token usage and intermediate data are not directly accessible within the same workflow. This monitoring workflow bridges that gap by:
Retrieving execution data using the execution ID
Extracting all LLM usage from any nested structure
Calculating costs with customizable pricing
Providing detailed analytics per node and model
WARNING: it works after the full execution of the workflow (i.e. you can't get this data before completion of all tasks in the workflow)

⚙️ Setup Instructions

Prerequisites
Experience Required: Basic familiarity with n8n LLM nodes and AI agents
Agent Configuration: In your monitored workflows, go to agent settings and enable "Return Intermediate Steps"
For getting execution data, you need to set upthe n8n API in your instance (also available onthe free version)

Installation Steps
Import this monitoring workflow into your n8n instance
Go to Settings >> select n8n API from left bar >> define an API. Now you can add this as the credential for your "Get an Execution" node
Configure your model name mappings in the "Standardize Names" node
Update model pricing in the "Model Prices" node (prices per 1M tokens)
To monitor a workflow:
Add an "Execute Workflow" node at the end of your target workflow
Select this monitoring workflow
Important: Turn OFF "Wait For Sub-Workflow Completion"
Pass the execution ID as input

🔧 Customization

When You See Errors
If the workflow enters the error path, it means an undefined model was detected. Simply:
Add the model name to the standardize_names_dic
Add its pricing to the model_price_dic
Re-run the workflow

Configurable Elements
Model Name Mapping**: Standardize different model name variations (e.g., "gpt-4-0613" → "gpt-4")
Pricing Dictionary**: Set costs per million tokens for input/output
Extraction Depth**: Captures tokens from any nesting level automatically

📊 Output Data

Per LLM Call
Cost Breakdown**: Prompt, completion, and total costs in USD
Token Metrics**: Prompt tokens, completion tokens, total tokens
Performance**: Execution time, start time, finish reason
Content Preview**: First 100 chars of input/output for debugging
Model Parameters**: Temperature, max tokens, timeout, retry count
Execution Context**: Workflow name, node name, execution status
Flow Tracking**: Previous nodes chain

Summary Statistics
Total executions and costs
Breakdown by model type
Breakdown by node
Average cost per call
Total execution time

✨ Key Benefits

No External Dependencies**: Everything runs within n8n
Universal Compatibility**: Works with any workflow structure
Automatic Detection**: Finds LLM usage regardless of nesting
Real-time Monitoring**: Track costs as workflows execute
Debugging Support**: Preview actual prompts and responses
Scalable**: Handles multiple models and complex workflows

📝 Example Use Cases

Cost Optimization**: Identify expensive nodes and optimize prompts
Usage Analytics**: Track token consumption across teams/projects
Budget Monitoring**: Set alerts based on cost thresholds
Performance Analysis**: Find slow-running LLM calls
Debugging**: Review actual inputs/outputs without logs
Compliance**: Audit AI usage across your organization

🚀 Quick Start

Import workflow
Update model prices (if needed)
Add monitoring to any workflow with the Execute Workflow node
View detailed cost breakdowns instantly

Note: Prices are configured per million tokens. Default includes GPT-4, GPT-3.5, Claude, and other popular models. Add custom models as needed.