Ai Model Usage Dashboard Track Token Metrics And Costs For Llm Workflows 9497 – n8n Workflows – Free Template

What This Workflow Does

This n8n workflow creates an AI-powered dashboard that tracks and visualizes data from your chat interactions and LLM API usage. It collects real-time metrics including message counts, active sessions, token consumption, and operational costs across different AI models. The dashboard provides an interactive interface to monitor your AI workflow performance with key performance indicators displayed in an easy-to-understand format.

How It Works

The workflow operates through a multi-stage process that captures chat interactions and processes them through an AI agent. When a chat trigger fires, the workflow routes the message through an LLM chat interface powered by OpenAI. A memory buffer window maintains conversation context across multiple exchanges. The system then aggregates usage data including token counts and associated costs, storing this information in a structured format. A scheduled trigger periodically processes batches of data, while a webhook endpoint allows external systems to submit additional data. Finally, the workflow renders an interactive HTML dashboard that displays all collected metrics in real-time.

Use Cases

• Monitor AI chatbot usage and costs across multiple customer interactions to optimize your API spending
• Track token consumption patterns by different AI models to identify which models are most cost-efficient for your workloads
• Analyze session analytics and conversation metrics to improve user engagement and chatbot performance
• Create billing reports and cost allocation data for AI API usage across different departments or projects
• Visualize real-time performance metrics of your AI workflows to identify bottlenecks and optimization opportunities

Nodes Used

• Chat Trigger: Initiates the workflow when users send messages to the chat interface
• Agent: Orchestrates the AI workflow logic and decision-making processes
• Memory Buffer Window: Maintains conversation history and context for multi-turn interactions
• LM Chat OpenAI: Connects to OpenAI’s language models for chat completion and responses
• Schedule Trigger: Executes data processing tasks on a predefined schedule
• Split in Batches: Divides large datasets into manageable chunks for processing
• Webhook: Receives external data submissions and integrations
• Merge: Combines data from multiple workflow branches
• Code: Executes custom JavaScript logic for data transformation
• Data Table: Displays collected metrics in structured table format
• Respond to Webhook: Sends responses back to external API calls
• Set: Assigns variables and prepares data for downstream nodes
• NoOp: No-operation node for workflow control and branching
• Sticky Note: Documentation and notes within the workflow canvas

Prerequisites

• Active n8n account with access to workflow creation and deployment
• OpenAI API key with appropriate permissions for chat completion endpoints
• Basic understanding of AI workflow concepts and LLM pricing models
• Access to a web server or n8n instance to host the interactive dashboard
• Database or storage system configured to persist workflow execution data and metrics

Difficulty Level

Intermediate. This workflow requires familiarity with n8n node configuration, API integration concepts, and basic data processing. Users should understand how to work with LLM APIs and configure memory management for chat applications. The setup involves connecting external services and configuring webhook endpoints, which assumes moderate technical proficiency but does not require advanced programming skills.

This workflow template is shared under the n8n fair-code license. Free to use and modify.