π Token Estim8r UI β visualize token usage analytics dashboard in n8n
π Visualize all your AI Token Usage analytics Dashboard using a single n8n Workflow
Artwork Generated with β¨ ideoGener8r n8n workflow template
Token Estim8r UI is the premium version of our token tracking solution for n8n users who want real-time insight into AI model usage and exact cost per execution β all in a beautifully designed analytics dashboard.
We've done the work with over 4000 lines of code for you to simply add a pre-configured HTTP Request node to the end of any workflow you want to track, and Token Estim8r UI will handle the rest: collecting data, analyzing token usage, calculating model costs, and feeding everything into a clean UI with charts, graphs, and summaries.
πΌοΈ What the Dashboard Looks Like

πββοΈ Who is this for?
This workflow is perfect for:
- AI engineers
- Automation specialists
- Business analysts
- Teams using OpenAI, Anthropic, Claude, or any token-based LLM
If youβre managing API budgets or optimizing prompt performance, this tool provides immediate visibility into where tokens (and money) are going.
π What problem does this solve?
n8n makes it easy to build powerful workflows β but it doesnβt natively track OpenAI token usage or cost. Without that visibility, itβs impossible to:
- Know what each automation is costing
- Spot inefficiencies in prompt construction
- Track cost trends over time
Token Estim8r UI solves that with zero manual logging.
βοΈ What this workflow does
- Fetches detailed execution logs from n8n
- Extracts prompt/completion token usage per model/tool
- Optionally retrieves live pricing or use preset pricing as of 4/2025
- Calculates total cost per run
- Sends data to a backend for aggregation
- Displays a full-featured analytics dashboard with:
- Total tokens, cost, and usage trends
- Most used models/tools
- Workflow-model correlations
- Cost breakdowns and comparisons
- Workflow usage over time
- Auto-complete workflow search with filtering by ID or name
- Filter by date or workflow (single or all workflows)
- Built in image server
- Sortable & searchable data table of filtered results with:
- Prompt & completion token breakdown
- Cost calculations
- Workflow name + direct link to the workflow
- Link to exact execution in n8n
π οΈ How Setup Works
- Import the Token Estim8r UI workflow into your n8n instance
- Deploy the included dashboard (React/Next.js app, hosted or self-hosted)
- Configure Google Sheets or your preferred backend (e.g., Supabase, Airtable)
- Copy the prebuilt HTTP Request node into the end of any n8n workflow
- Run your workflow β data is captured, aggregated and stored automatically in Google Sheets π
π What Makes This Better than the simple version?
The simple version logs to Google Sheets only. This premium UI version adds:
- Full analytics dashboard
- Cost aggregation across all workflows
- Graphs, filters, and model/tool comparisons
π§ Customization Ideas
- Switch backend to Supabase or Firebase
- Add alerts via Slack when costs exceed thresholds
- Build weekly token cost summaries
- Track model performance across teams
- Add filters by user/session/timeframe
π§ Why Users Love It
"Token Estim8r UI is exactly what I needed to take control of my AI costs inside n8n. Itβs plug and play β and the dashboard makes optimization easy."
β Beta user, AI Ops Lead
π If you're serious about building with AI in n8n,
Token Estim8r UI gives you the visibility to scale confidently. π
n8n Token Usage Analytics Dashboard
This n8n workflow provides a robust solution for collecting, processing, and visualizing token usage analytics. It acts as a backend for a dashboard, allowing you to track and analyze how tokens are being consumed, likely in an AI or API context.
The workflow listens for incoming token usage data via a webhook, processes it, and then stores it in a Google Sheet. It also includes functionality to generate a CSV file from the processed data, which can be used for further analysis or visualization.
What it does
This workflow automates the following steps:
- Receives Token Usage Data: It starts by listening for incoming POST requests to a webhook URL, expecting token usage data in the request body.
- Processes Incoming Data: It extracts relevant fields from the incoming JSON payload and transforms them into a structured format.
- Appends to Google Sheet: The processed token usage data is then appended as a new row to a specified Google Sheet, serving as a persistent data store.
- Generates CSV for Visualization: It reads all data from the Google Sheet, converts it into a CSV file, and then responds to the initial webhook request with this CSV data. This allows an external application or dashboard to easily consume and visualize the latest token usage.
- Includes a Wait Step (Optional/Placeholder): A "Wait" node is present in the workflow, which could be used to introduce a delay if there are rate limits or processing requirements between steps, though its current configuration is not detailed in the JSON.
Prerequisites/Requirements
To use this workflow, you will need:
- n8n Instance: A running n8n instance to import and execute the workflow.
- Google Sheets Account: A Google Sheets spreadsheet to store the token usage data. You will need to configure a Google Sheets credential in n8n with access to this spreadsheet.
- Webhook Trigger: An external system or application that sends token usage data (as JSON in a POST request) to the n8n webhook URL.
Setup/Usage
-
Import the Workflow:
- Download the provided JSON file.
- In your n8n instance, click "Workflows" in the left sidebar.
- Click "New" and then "Import from JSON".
- Paste the workflow JSON or upload the file.
-
Configure Credentials:
- Google Sheets:
- Locate the "Google Sheets" node.
- Click on the "Credential" field and select an existing Google Sheets credential or create a new one. Ensure the credential has write access to your target spreadsheet.
- Enter the "Spreadsheet ID" and "Sheet Name" where you want to store the data.
- Webhook:
- Locate the "Webhook" node.
- Copy the "Webhook URL". This is the URL you will use to send token usage data to this workflow.
- Google Sheets:
-
Configure Nodes:
- Edit Fields (Set): Review the "Edit Fields" node to ensure the field mapping matches the incoming data structure and your desired output for the Google Sheet.
- Code: Review the "Code" node for any custom logic or data transformations.
- HTML: Review the "HTML" node for any specific HTML templating or data extraction.
- Convert to File: Review the "Convert to File" node to ensure the CSV conversion settings (e.g., delimiter, header) are as desired.
- Wait: If the "Wait" node is configured, adjust the delay as needed.
-
Activate the Workflow:
- Once configured, activate the workflow by toggling the "Active" switch in the top right corner of the workflow editor.
-
Send Data to the Webhook:
- Send POST requests containing your token usage data (JSON payload) to the Webhook URL you copied in step 2. The workflow will then process and store this data.
-
Access Analytics:
- The latest token usage data in CSV format will be returned as the response to the webhook call, allowing your dashboard or application to consume it directly.
- You can also directly view the accumulated data in your configured Google Sheet.
Related Templates
Track competitor SEO keywords with Decodo + GPT-4.1-mini + Google Sheets
This workflow automates competitor keyword research using OpenAI LLM and Decodo for intelligent web scraping. Who this is for SEO specialists, content strategists, and growth marketers who want to automate keyword research and competitive intelligence. Marketing analysts managing multiple clients or websites who need consistent SEO tracking without manual data pulls. Agencies or automation engineers using Google Sheets as an SEO data dashboard for keyword monitoring and reporting. What problem this workflow solves Tracking competitor keywords manually is slow and inconsistent. Most SEO tools provide limited API access or lack contextual keyword analysis. This workflow solves that by: Automatically scraping any competitorβs webpage with Decodo. Using OpenAI GPT-4.1-mini to interpret keyword intent, density, and semantic focus. Storing structured keyword insights directly in Google Sheets for ongoing tracking and trend analysis. What this workflow does Trigger β Manually start the workflow or schedule it to run periodically. Input Setup β Define the website URL and target country (e.g., https://dev.to, france). Data Scraping (Decodo) β Fetch competitor web content and metadata. Keyword Analysis (OpenAI GPT-4.1-mini) Extract primary and secondary keywords. Identify focus topics and semantic entities. Generate a keyword density summary and SEO strength score. Recommend optimization and internal linking opportunities. Data Structuring β Clean and convert GPT output into JSON format. Data Storage (Google Sheets) β Append structured keyword data to a Google Sheet for long-term tracking. Setup Prerequisites If you are new to Decode, please signup on this link visit.decodo.com n8n account with workflow editor access Decodo API credentials OpenAI API key Google Sheets account connected via OAuth2 Make sure to install the Decodo Community node. Create a Google Sheet Add columns for: primarykeywords, seostrengthscore, keyworddensity_summary, etc. Share with your n8n Google account. Connect Credentials Add credentials for: Decodo API credentials - You need to register, login and obtain the Basic Authentication Token via Decodo Dashboard OpenAI API (for GPT-4o-mini) Google Sheets OAuth2 Configure Input Fields Edit the βSet Input Fieldsβ node to set your target site and region. Run the Workflow Click Execute Workflow in n8n. View structured results in your connected Google Sheet. How to customize this workflow Track Multiple Competitors β Use a Google Sheet or CSV list of URLs; loop through them using the Split In Batches node. Add Language Detection β Add a Gemini or GPT node before keyword analysis to detect content language and adjust prompts. Enhance the SEO Report β Expand the GPT prompt to include backlink insights, metadata optimization, or readability checks. Integrate Visualization β Connect your Google Sheet to Looker Studio for SEO performance dashboards. Schedule Auto-Runs β Use the Cron Node to run weekly or monthly for competitor keyword refreshes. Summary This workflow automates competitor keyword research using: Decodo for intelligent web scraping OpenAI GPT-4.1-mini for keyword and SEO analysis Google Sheets for live tracking and reporting Itβs a complete AI-powered SEO intelligence pipeline ideal for teams that want actionable insights on keyword gaps, optimization opportunities, and content focus trends, without relying on expensive SEO SaaS tools.
Generate song lyrics and music from text prompts using OpenAI and Fal.ai Minimax
Spark your creativity instantly in any chatβturn a simple prompt like "heartbreak ballad" into original, full-length lyrics and a professional AI-generated music track, all without leaving your conversation. π What This Template Does This chat-triggered workflow harnesses AI to generate detailed, genre-matched song lyrics (at least 600 characters) from user messages, then queues them for music synthesis via Fal.ai's minimax-music model. It polls asynchronously until the track is ready, delivering lyrics and audio URL back in chat. Crafts original, structured lyrics with verses, choruses, and bridges using OpenAI Submits to Fal.ai for melody, instrumentation, and vocals aligned to the style Handles long-running generations with smart looping and status checks Returns complete song package (lyrics + audio link) for seamless sharing π§ Prerequisites n8n account (self-hosted or cloud with chat integration enabled) OpenAI account with API access for GPT models Fal.ai account for AI music generation π Required Credentials OpenAI API Setup Go to platform.openai.com β API keys (sidebar) Click "Create new secret key" β Name it (e.g., "n8n Songwriter") Copy the key and add to n8n as "OpenAI API" credential type Test by sending a simple chat completion request Fal.ai HTTP Header Auth Setup Sign up at fal.ai β Dashboard β API Keys Generate a new API key β Copy it In n8n, create "HTTP Header Auth" credential: Name="Fal.ai", Header Name="Authorization", Header Value="Key [Your API Key]" Test with a simple GET to their queue endpoint (e.g., /status) βοΈ Configuration Steps Import the workflow JSON into your n8n instance Assign OpenAI API credentials to the "OpenAI Chat Model" node Assign Fal.ai HTTP Header Auth to the "Generate Music Track", "Check Generation Status", and "Fetch Final Result" nodes Activate the workflowβchat trigger will appear in your n8n chat interface Test by messaging: "Create an upbeat pop song about road trips" π― Use Cases Content Creators: YouTubers generating custom jingles for videos on the fly, streamlining production from idea to audio export Educators: Music teachers using chat prompts to create era-specific folk tunes for classroom discussions, fostering interactive learning Gift Personalization: Friends crafting anniversary R&B tracks from shared memories via quick chats, delivering emotional audio surprises Artist Brainstorming: Songwriters prototyping hip-hop beats in real-time during sessions, accelerating collaboration and iteration β οΈ Troubleshooting Invalid JSON from AI Agent: Ensure the system prompt stresses valid JSON; test the agent standalone with a sample query Music Generation Fails (401/403): Verify Fal.ai API key has minimax-music access; check usage quotas in dashboard Status Polling Loops Indefinitely: Bump wait time to 45-60s for complex tracks; inspect fal.ai queue logs for bottlenecks Lyrics Under 600 Characters: Tweak agent prompt to enforce fuller structures like [V1][C][V2][B][C]; verify output length in executions
Automate invoice processing with OCR, GPT-4 & Salesforce opportunity creation
PDF Invoice Extractor (AI) End-to-end pipeline: Watch Drive β Download PDF β OCR text β AI normalize to JSON β Upsert Buyer (Account) β Create Opportunity β Map Products β Create OLI via Composite API β Archive to OneDrive. --- Node by node (what it does & key setup) 1) Google Drive Trigger Purpose: Fire when a new file appears in a specific Google Drive folder. Key settings: Event: fileCreated Folder ID: google drive folder id Polling: everyMinute Creds: googleDriveOAuth2Api Output: Metadata { id, name, ... } for the new file. --- 2) Download File From Google Purpose: Get the file binary for processing and archiving. Key settings: Operation: download File ID: ={{ $json.id }} Creds: googleDriveOAuth2Api Output: Binary (default key: data) and original metadata. --- 3) Extract from File Purpose: Extract text from PDF (OCR as needed) for AI parsing. Key settings: Operation: pdf OCR: enable for scanned PDFs (in options) Output: JSON with OCR text at {{ $json.text }}. --- 4) Message a model (AI JSON Extractor) Purpose: Convert OCR text into strict normalized JSON array (invoice schema). Key settings: Node: @n8n/n8n-nodes-langchain.openAi Model: gpt-4.1 (or gpt-4.1-mini) Message role: system (the strict prompt; references {{ $json.text }}) jsonOutput: true Creds: openAiApi Output (per item): $.message.content β the parsed JSON (ensure itβs an array). --- 5) Create or update an account (Salesforce) Purpose: Upsert Buyer as Account using an external ID. Key settings: Resource: account Operation: upsert External Id Field: taxid_c External Id Value: ={{ $json.message.content.buyer.tax_id }} Name: ={{ $json.message.content.buyer.name }} Creds: salesforceOAuth2Api Output: Account record (captures Id) for downstream Opportunity. --- 6) Create an opportunity (Salesforce) Purpose: Create Opportunity linked to the Buyer (Account). Key settings: Resource: opportunity Name: ={{ $('Message a model').item.json.message.content.invoice.code }} Close Date: ={{ $('Message a model').item.json.message.content.invoice.issue_date }} Stage: Closed Won Amount: ={{ $('Message a model').item.json.message.content.summary.grand_total }} AccountId: ={{ $json.id }} (from Upsert Account output) Creds: salesforceOAuth2Api Output: Opportunity Id for OLI creation. --- 7) Build SOQL (Code / JS) Purpose: Collect unique product codes from AI JSON and build a SOQL query for PricebookEntry by Pricebook2Id. Key settings: pricebook2Id (hardcoded in script): e.g., 01sxxxxxxxxxxxxxxx Source lines: $('Message a model').first().json.message.content.products Output: { soql, codes } --- 8) Query PricebookEntries (Salesforce) Purpose: Fetch PricebookEntry.Id for each Product2.ProductCode. Key settings: Resource: search Query: ={{ $json.soql }} Creds: salesforceOAuth2Api Output: Items with Id, Product2.ProductCode (used for mapping). --- 9) Code in JavaScript (Build OLI payloads) Purpose: Join lines with PBE results and Opportunity Id β build OpportunityLineItem payloads. Inputs: OpportunityId: ={{ $('Create an opportunity').first().json.id }} Lines: ={{ $('Message a model').first().json.message.content.products }} PBE rows: from previous node items Output: { body: { allOrNone:false, records:[{ OpportunityLineItem... }] } } Notes: Converts discount_total β per-unit if needed (currently commented for standard pricing). Throws on missing PBE mapping or empty lines. --- 10) Create Opportunity Line Items (HTTP Request) Purpose: Bulk create OLIs via Salesforce Composite API. Key settings: Method: POST URL: https://<your-instance>.my.salesforce.com/services/data/v65.0/composite/sobjects Auth: salesforceOAuth2Api (predefined credential) Body (JSON): ={{ $json.body }} Output: Composite API results (per-record statuses). --- 11) Update File to One Drive Purpose: Archive the original PDF in OneDrive. Key settings: Operation: upload File Name: ={{ $json.name }} Parent Folder ID: onedrive folder id Binary Data: true (from the Download node) Creds: microsoftOneDriveOAuth2Api Output: Uploaded file metadata. --- Data flow (wiring) Google Drive Trigger β Download File From Google Download File From Google β Extract from File β Update File to One Drive Extract from File β Message a model Message a model β Create or update an account Create or update an account β Create an opportunity Create an opportunity β Build SOQL Build SOQL β Query PricebookEntries Query PricebookEntries β Code in JavaScript Code in JavaScript β Create Opportunity Line Items --- Quick setup checklist π Credentials: Connect Google Drive, OneDrive, Salesforce, OpenAI. π IDs: Drive Folder ID (watch) OneDrive Parent Folder ID (archive) Salesforce Pricebook2Id (in the JS SOQL builder) π§ AI Prompt: Use the strict system prompt; jsonOutput = true. π§Ύ Field mappings: Buyer tax id/name β Account upsert fields Invoice code/date/amount β Opportunity fields Product name must equal your Product2.ProductCode in SF. β Test: Drop a sample PDF β verify: AI returns array JSON only Account/Opportunity created OLI records created PDF archived to OneDrive --- Notes & best practices If PDFs are scans, enable OCR in Extract from File. If AI returns non-JSON, keep βReturn only a JSON arrayβ as the last line of the prompt and keep jsonOutput enabled. Consider adding validation on parsing.warnings to gate Salesforce writes. For discounts/taxes in OLI: Standard OLI fields donβt support per-line discount amounts directly; model them in UnitPrice or custom fields. Replace the Composite API URL with your orgβs domain or use the Salesforce nodeβs Bulk Upsert for simplicity.