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

Name: Track LLM costs and usage across OpenAI, Anthropic, Google and more
Availability: InStock
Author: Aldayel

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

Open on n8n.io

$20/month : Unlimited workflows

2500 executions/month

Try free

THE #1 IN WEB SCRAPING

Scrape any website without limits

Try free

HOSTINGER

Early Deal
DISCOUNT 20%

Self-hosted n8n

Unlimited workflows - from $4.99/mo

Try free

#1 hub for scraping, AI & automation

6000+ actors - $5 credits/mo

Try free

Overview

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

Aldayel

1 workflow

Complexity

advanced

Published 31 Mar 2026

Likes 0

View on n8n.io Download Workflow

Install path: /data/workflows/14536/14536.json

Share Your Workflow

Have a useful automation to share? Publish it and help the community.

Submit Your Template How to Submit

Related Workflows

Forecast property CAPEX and ROI weekly using Google Sheets and GPT-4o

## How It Works This workflow automates weekly capital expenditure (CAPEX) forecasting for property portfolios using a multi-agent AI architecture. It targets property managers, asset managers, and facilities finance teams who need data-driven maintenance budgeting without manual spreadsheet analysis. Three Google Sheets sources, namely: maintenance records, property data, and tenant feedback, are merged into a unified dataset. A Main Prediction Agent orchestrates three specialist sub-agents: a CAPEX Prioritizer that ranks spending needs, an ROI Simulator that models return scenarios, and a Quote Requester that fetches vendor estimates. Each agent is backed by dedicated AI models, memory, and tools including a Calculator and Financial Modeling Tool. Structured predictions are parsed, split by category, formatted, saved back to Google Sheets, and pushed to an external budgeting system via POST, delivering a fully automated, auditable CAPEX planning pipeline every week. ## Setup Steps 1. Connect Google Sheets credentials to all three read nodes and the Save Predictions node. 2. Set correct Sheet IDs for maintenance, property, and tenant feedback tabs. 3. Add Claude or OpenAI API credentials to all Chat Model nodes. 4. Configure the Financial Modeling Tool with your cost rate assumptions. 5. Replace the POST placeholder URL in Update Budgeting System with your actual endpoint. ## Prerequisites - Google Sheets account with populated maintenance, property, and tenant data - Claude or OpenAI API credentials - External budgeting system with a POST-compatible API endpoint ## Use Cases - Weekly CAPEX forecasting for multi-property real estate portfolios - Automated ROI modelling for planned renovations or equipment replacement ## Customization Add more data sources (e.g., IoT sensors, ERP exports). ## Benefits Eliminates manual CAPEX spreadsheet work with autonomous AI forecasting.

View

Turn support tickets into developer insights with OpenAI, Postgres, Slack and Jira

## Overview This workflow transforms raw support tickets into actionable developer insights using AI and data processing. It automatically detects recurring issues, identifies root causes, ranks severity, and generates a structured engineering report. By combining embeddings, clustering, and AI analysis, it helps teams prioritize bugs, understand user pain points, and take data-driven product decisions. --- ## How It Works 1. **Scheduled Trigger** - Runs automatically at a defined time (e.g., daily). 2. **Workflow Configuration** - Defines time window, similarity threshold, scoring weights, and delivery options. 3. **Fetch Feedback Data** - Retrieves recent support tickets (bugs and feature requests) from Postgres. 4. **Preprocessing** - Cleans, normalizes, and removes duplicate messages. 5. **Embedding & Clustering** - Generates embeddings using OpenAI. - Groups similar tickets using cosine similarity. 6. **Cluster Aggregation** - Combines related tickets into structured clusters. 7. **Root Cause Analysis** - AI agent analyzes clusters to identify: - Root cause - Impacted module - Severity - Debug steps - Fix direction 8. **Severity Scoring** - Calculates weighted score based on: - Frequency - Sentiment - Churn risk - Enterprise impact 9. **Report Generation** - Generates a developer-focused report including: - Executive summary - Ranked bugs - Feature requests - Risk analysis - Sprint priorities 10. **Delivery** - Sends report to Slack - Optionally creates Jira issues - Optional email delivery --- ## Setup Instructions 1. **Database Setup** - Configure Postgres credentials - Ensure `support_tickets` table exists with required fields 2. **OpenAI Configuration** - Add API key for: - Embeddings (text-embedding-3-small) - AI analysis agents 3. **Slack Integration** - Add Slack credentials - Set channel ID 4. **Email Setup (Optional)** - Configure SMTP or email service 5. **Jira Integration (Optional)** - Add Jira credentials - Set project key and issue type 6. **Customize Parameters** - Adjust: - Similarity threshold - Scoring weights - Time window 7. **Schedule Configuration** - Modify trigger timing as needed --- ## Use Cases - Product teams analyzing user feedback at scale - Engineering teams prioritizing bug fixes - SaaS companies tracking churn-related issues - Customer support insights automation - AI-driven product intelligence dashboards --- ## Requirements - OpenAI API key - Postgres database with support ticket data - Slack (optional) - Email service (optional) - Jira account (optional) - n8n instance --- ## Key Features - Automated feedback clustering using embeddings - AI-driven root cause analysis - Weighted severity scoring system - Developer-ready intelligence reports - Multi-channel delivery (Slack, Email, Jira) - Fully customizable scoring and thresholds --- ## Summary A powerful AI-driven workflow that converts raw support tickets into structured developer intelligence. It automates clustering, root cause detection, prioritization, and reporting helping teams fix the right problems faster and build better products.

View

Combine answers from OpenAI, Anthropic, Gemini and Groq into one consensus

## Stop trusting one model. Let multiple LLMs show you where they agree and where they don't. Ask the same question to multiple LLMs and get one answer you can actually trust. Instead of hoping one model gets it right, this workflow sends your question to four models at once, compares what they say, and catches the ones that sound confident but are probably wrong. This is not a "chain models together" template. Instead of trusting one model's answer, it makes multiple models prove they agree by checking every answer against the others and showing you exactly how much they align. ### How it works The workflow runs in four stages: 1. **Ask in parallel:** Your question goes to four LLMs at the same time. Each model answers on its own and reports how confident it is. No model sees what the others said. 2. **Compare answers:** A similarity engine checks how much the answers actually agree. It uses two different methods (Jaccard and Cosine) plus extra checks for short answers. So if one model says "4" and another says "The answer is 4," both get credit for agreeing. 3. **Calibrate confidence:** This is the key part. The system looks at what each model claims versus what the others actually said. A model saying it is 95% sure while everyone else disagrees? Its confidence gets cut. A model that is unsure but matches what the group said? Its confidence goes up. Overconfident outliers are usually the first sign of a hallucination. 4. **Deliver the result:** If models agree, you get a single weighted answer with a visual bar showing how strong the agreement is. If they properly disagree, the system switches to peer review mode and shows every answer so you can decide for yourself. ### Key Benefits - **Catches hallucinations with maths, not prompts.** An overconfident model that disagrees with the group gets its score reduced automatically. - **Three clear tiers.** Strong agreement gets a green label. Partial agreement gets yellow. Weak agreement gets orange. You always know how much to trust the response. - **Works with any LLM you want.** Default setup uses OpenAI, Anthropic, Gemini, and Groq. Swap any of them or add more. - **Tells you when a model fails.** If one provider is down or not set up yet, the response says so instead of breaking silently. ### Setup - Add your API credentials. - Activate the workflow and open the production chat URL. - Type any question and wait for the consensus analysis ### Who this is for - AI engineers comparing model reliability across different providers - Product teams that need dependable AI answers for things users will see - Researchers looking at how different LLMs handle the same question - Anyone who has been burned by one model confidently making things up ### Required APIs & Credentials Add credentials for the LLM providers you want to use. The default setup includes OpenAI, Anthropic, Google Gemini, and Groq, but you can swap or remove any of them. ### How to customise it - **Swap models:** Replace any LLM node with a different provider. Add more branches if you want and update the Merge node input count. - **Adjust the calibration:** Open the Confidence Calibration node and change what counts as overconfident, underconfident, or divergent. - **Change the agreement tiers:** In the Format Chat Message node, the defaults are green at 70%, yellow at 40%, orange below that. - **Use a different trigger:** Replace the chat trigger with a webhook, Slack command, or scheduled trigger. - **Send the output somewhere:** The structured JSON from Format Final Output works with Google Sheets, databases, dashboards, or any other workflow. ### Known limitations This workflow picks the answer most models agree on. That works well for factual questions. But if three models share the same wrong answer and one model gets it right, the correct answer gets penalised for being the outlier. For trick questions or topics where popular knowledge is wrong, keep that in mind.

View

Need Custom Automation?

Get help designing a custom n8n workflow that connects your stack and fits your process.

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

Workflow preview

Overview

Installation Steps

To Monitor a Workflow

Prerequisites

Supported Providers

120+ Model Variations Mapped

Output Data

Per LLM Call

Summary Statistics

💡 You can do anything with this data!