Track LLM costs and usage across OpenAI, Anthropic, Google and more

Installation Steps

Go to Settings → n8n API and create an API key Add it as credential for the Get Execution Data node Review model mappings in Standardize Names node Review pricing in Model Prices node

To Monitor a Workflow

Add Execute Workflow node at the end of your target workflow Select this monitoring workflow Turn OFF "Wait For Sub-Workflow Completion" Pass { "executionId": "{{ $execution.id }}" } as input

Prerequisites

Enable "Return Intermediate Steps" in your AI Agent settings for best results.

Supported Providers

OpenAI · Anthropic · Google · DeepSeek · Meta · Mistral · xAI · Cohere · Alibaba Qwen · Moonshot Kimi

120+ Model Variations Mapped Includes all versioned variants (e.g., gpt-4o-2024-08-06 → gpt-4o)

Prices sourced from official provider pages (March 2026)

Output Data

Per LLM Call Cost Breakdown (prompt, completion, total USD) Token Metrics (prompt, completion, total) Performance (execution time, finish reason) Content Preview (first 100 chars I/O) Model Parameters (temp, max tokens, timeout) Execution Context (workflow, node, 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

💡 You can do anything with this data!

Store in a database for historical tracking Send to Teams as a cost alert Build dashboards with the summary data Set budget thresholds and trigger warnings Export to Google Sheets for reporting