This workflow follows the HTTP Request → Postgres recipe pattern — see all workflows that pair these two integrations.
The workflow JSON
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{
"active": false,
"connections": {
"Workflow Trigger": {
"main": [
[
{
"node": "Process Execution Data",
"type": "main",
"index": 0
}
]
]
},
"Create Usage Table": {
"main": [
[
{
"node": "Create Pricing Table",
"type": "main",
"index": 0
}
]
]
},
"Create Pricing Table": {
"main": [
[
{
"node": "Insert Model Pricing",
"type": "main",
"index": 0
}
]
]
},
"Insert Model Pricing": {
"main": [
[
{
"node": "Create Cost View",
"type": "main",
"index": 0
}
]
]
},
"Process Execution Data": {
"main": [
[
{
"node": "Filter LLM Usage",
"type": "main",
"index": 0
}
]
]
},
"Filter LLM Usage": {
"main": [
[
{
"node": "Insert Usage Data",
"type": "main",
"index": 0
}
]
]
},
"Insert Usage Data": {
"main": [
[
{
"node": "Generate Weekly Report",
"type": "main",
"index": 0
}
]
]
},
"LLM Usage Webhook": {
"main": [
[
{
"node": "Process Webhook Data",
"type": "main",
"index": 0
}
]
]
},
"Process Webhook Data": {
"main": [
[
{
"node": "Insert Usage Data",
"type": "main",
"index": 0
}
]
]
},
"Generate Weekly Report": {
"main": [
[
{
"node": "Grafana Queries Reference",
"type": "main",
"index": 0
}
]
]
}
},
"createdAt": "2025-09-14T01:52:18.680Z",
"id": "xFOifn4m31oXP9Ji",
"isArchived": false,
"meta": null,
"name": "My workflow 69",
"nodes": [
{
"parameters": {},
"id": "e51b7db5-7b15-4791-967a-8fd159d2e6c9",
"name": "Workflow Trigger",
"type": "n8n-nodes-base.n8nTrigger",
"typeVersion": 1,
"position": [
80,
160
]
},
{
"parameters": {
"operation": "executeQuery",
"query": "-- Criar tabela de uso de LLM\nCREATE TABLE IF NOT EXISTS llm_usage (\n id BIGSERIAL PRIMARY KEY,\n ts TIMESTAMPTZ NOT NULL,\n workflow_id TEXT NOT NULL,\n workflow_name TEXT NOT NULL,\n run_id TEXT NOT NULL,\n node_name TEXT,\n provider TEXT NOT NULL,\n model TEXT NOT NULL,\n input_tokens INTEGER NOT NULL DEFAULT 0,\n output_tokens INTEGER NOT NULL DEFAULT 0,\n total_tokens INTEGER NOT NULL DEFAULT 0,\n latency_ms INTEGER,\n status TEXT DEFAULT 'success',\n region TEXT DEFAULT 'BR-SP',\n user_id TEXT,\n cost_usd NUMERIC(12,6) DEFAULT 0\n);",
"options": {}
},
"id": "987fcaf2-b829-4f8e-aa62-b382e24c98b2",
"name": "Create Usage Table",
"type": "n8n-nodes-base.postgres",
"typeVersion": 2.5,
"position": [
288,
48
],
"executeOnce": true
},
{
"parameters": {
"operation": "executeQuery",
"query": "-- Criar tabela de pre\u00e7os dos modelos\nCREATE TABLE IF NOT EXISTS model_pricing (\n provider TEXT NOT NULL,\n model TEXT NOT NULL,\n prompt_price_per_1k NUMERIC(12,6) NOT NULL,\n completion_price_per_1k NUMERIC(12,6) NOT NULL,\n currency TEXT DEFAULT 'USD',\n PRIMARY KEY (provider, model)\n);",
"options": {}
},
"id": "3a50c2bc-d7d5-4c5a-a174-19ddb8271889",
"name": "Create Pricing Table",
"type": "n8n-nodes-base.postgres",
"typeVersion": 2.5,
"position": [
288,
208
],
"executeOnce": true
},
{
"parameters": {
"operation": "executeQuery",
"query": "-- Inserir pre\u00e7os dos modelos OpenAI (atualize conforme necess\u00e1rio)\nINSERT INTO model_pricing (provider, model, prompt_price_per_1k, completion_price_per_1k) VALUES\n('openai', 'gpt-4o-mini', 0.00015, 0.0006),\n('openai', 'gpt-4o', 0.005, 0.015),\n('openai', 'gpt-4-turbo', 0.01, 0.03),\n('openai', 'gpt-3.5-turbo', 0.0005, 0.0015),\n('openai', 'text-embedding-3-small', 0.00002, 0.00002),\n('openai', 'text-embedding-3-large', 0.00013, 0.00013)\nON CONFLICT (provider, model) DO UPDATE SET\n prompt_price_per_1k = EXCLUDED.prompt_price_per_1k,\n completion_price_per_1k = EXCLUDED.completion_price_per_1k;",
"options": {}
},
"id": "a08e294f-a235-4f6e-9a93-ed1ec16e2c33",
"name": "Insert Model Pricing",
"type": "n8n-nodes-base.postgres",
"typeVersion": 2.5,
"position": [
288,
368
],
"executeOnce": true
},
{
"parameters": {
"operation": "executeQuery",
"query": "-- Criar view com custo calculado\nCREATE OR REPLACE VIEW v_llm_usage_cost AS\nSELECT\n u.*,\n COALESCE(u.input_tokens,0)/1000.0 * COALESCE(p.prompt_price_per_1k,0)\n + COALESCE(u.output_tokens,0)/1000.0 * COALESCE(p.completion_price_per_1k,0)\n AS total_cost_usd,\n CASE WHEN u.total_tokens = 0 THEN 0\n ELSE (COALESCE(u.input_tokens,0)/1000.0 * COALESCE(p.prompt_price_per_1k,0)\n + COALESCE(u.output_tokens,0)/1000.0 * COALESCE(p.completion_price_per_1k,0))\n / (u.total_tokens/1000.0)\n END AS usd_per_k_token\nFROM llm_usage u\nLEFT JOIN model_pricing p\n ON p.provider = u.provider\n AND p.model = u.model;",
"options": {}
},
"id": "81c647fb-b234-4bf9-ba0b-741b7e8b9d20",
"name": "Create Cost View",
"type": "n8n-nodes-base.postgres",
"typeVersion": 2.5,
"position": [
288,
528
],
"executeOnce": true
},
{
"parameters": {
"jsCode": "// Processar dados de execu\u00e7\u00e3o do workflow\nconst items = $input.all();\nconst processedItems = [];\n\nfor (const item of items) {\n const execution = item.json;\n \n // Extrair informa\u00e7\u00f5es b\u00e1sicas da execu\u00e7\u00e3o\n const executionData = {\n workflow_id: execution.workflowId || $workflow.id,\n workflow_name: execution.workflowName || $workflow.name,\n run_id: execution.id || $runId,\n ts: execution.startedAt || new Date().toISOString(),\n status: execution.finished ? 'success' : 'running',\n user_id: execution.userId || 'system'\n };\n \n // Procurar por n\u00f3s que usaram LLM\n if (execution.data && execution.data.resultData && execution.data.resultData.runData) {\n const runData = execution.data.resultData.runData;\n \n for (const nodeName in runData) {\n const nodeData = runData[nodeName];\n \n if (nodeData && nodeData.length > 0) {\n for (const nodeExecution of nodeData) {\n // Verificar se h\u00e1 dados de uso de LLM\n if (nodeExecution.data && nodeExecution.data.main && nodeExecution.data.main.length > 0) {\n for (const output of nodeExecution.data.main) {\n if (output && output.length > 0) {\n for (const outputItem of output) {\n // Procurar por dados de usage de LLM\n if (outputItem.json && (outputItem.json.usage || outputItem.json.token_usage)) {\n const usage = outputItem.json.usage || outputItem.json.token_usage;\n \n // Detectar provider e model\n let provider = 'openai'; // default\n let model = 'gpt-4o-mini'; // default\n \n // Tentar detectar provider baseado no nome do n\u00f3\n if (nodeName.toLowerCase().includes('openai')) provider = 'openai';\n if (nodeName.toLowerCase().includes('anthropic')) provider = 'anthropic';\n if (nodeName.toLowerCase().includes('gemini')) provider = 'google';\n \n // Tentar extrair model dos par\u00e2metros ou resposta\n if (outputItem.json.model) model = outputItem.json.model;\n if (nodeExecution.source && nodeExecution.source.main && nodeExecution.source.main[0] && nodeExecution.source.main[0][0] && nodeExecution.source.main[0][0].json && nodeExecution.source.main[0][0].json.model) {\n model = nodeExecution.source.main[0][0].json.model;\n }\n \n const llmUsage = {\n ...executionData,\n node_name: nodeName,\n provider: provider,\n model: model,\n input_tokens: usage.prompt_tokens || usage.input_tokens || 0,\n output_tokens: usage.completion_tokens || usage.output_tokens || 0,\n total_tokens: usage.total_tokens || ((usage.prompt_tokens || usage.input_tokens || 0) + (usage.completion_tokens || usage.output_tokens || 0)),\n latency_ms: nodeExecution.executionTime || 0\n };\n \n processedItems.push({ json: llmUsage });\n }\n }\n }\n }\n }\n }\n }\n }\n }\n}\n\nreturn processedItems.length > 0 ? processedItems : [{ json: { no_llm_usage: true } }];"
},
"id": "9d5c0794-65f3-45a7-9b33-68998d6f279d",
"name": "Process Execution Data",
"type": "n8n-nodes-base.code",
"typeVersion": 2,
"position": [
480,
160
]
},
{
"parameters": {
"conditions": {
"options": {
"caseSensitive": true,
"leftValue": "",
"typeValidation": "strict",
"version": 2
},
"conditions": [
{
"id": "has-llm-usage",
"leftValue": "={{ $json.no_llm_usage }}",
"rightValue": "",
"operator": {
"type": "boolean",
"operation": "false",
"singleValue": true
}
}
],
"combinator": "and"
},
"options": {}
},
"id": "a09dbd1e-d6cb-4788-97d8-10f5722da9ea",
"name": "Filter LLM Usage",
"type": "n8n-nodes-base.if",
"typeVersion": 2.2,
"position": [
688,
160
]
},
{
"parameters": {
"schema": {
"mappingMode": "defineBelow",
"value": {
"ts": "={{ $json.ts }}",
"workflow_id": "={{ $json.workflow_id }}",
"workflow_name": "={{ $json.workflow_name }}",
"run_id": "={{ $json.run_id }}",
"node_name": "={{ $json.node_name }}",
"provider": "={{ $json.provider }}",
"model": "={{ $json.model }}",
"input_tokens": "={{ $json.input_tokens }}",
"output_tokens": "={{ $json.output_tokens }}",
"total_tokens": "={{ $json.total_tokens }}",
"latency_ms": "={{ $json.latency_ms }}",
"status": "={{ $json.status }}",
"region": "BR-SP",
"user_id": "={{ $json.user_id }}"
}
},
"table": "llm_usage",
"options": {}
},
"id": "e950b309-65e8-4425-a6de-d9838ab89d48",
"name": "Insert Usage Data",
"type": "n8n-nodes-base.postgres",
"typeVersion": 2.5,
"position": [
880,
80
]
},
{
"parameters": {
"content": "# LLM Usage Monitoring System",
"height": 80,
"width": 800,
"color": 5
},
"id": "786b3944-da4f-4fff-9d4a-400b3f797177",
"name": "Sistema de Monitoramento LLM",
"type": "n8n-nodes-base.stickyNote",
"typeVersion": 1,
"position": [
0,
0
]
},
{
"parameters": {
"content": "## Configura\u00e7\u00e3o Inicial do Banco\n\nCria tabelas e views necess\u00e1rias para o monitoramento",
"height": 100,
"width": 400,
"color": 3
},
"id": "3fe326d2-75ac-4275-826c-b1844831e137",
"name": "Setup Note",
"type": "n8n-nodes-base.stickyNote",
"typeVersion": 1,
"position": [
160,
32
]
},
{
"parameters": {
"content": "## Processamento de Dados\n\nExtrai informa\u00e7\u00f5es de uso de LLM das execu\u00e7\u00f5es",
"height": 100,
"width": 350,
"color": 4
},
"id": "f426bf32-1925-455d-9d25-f1d3e10d928a",
"name": "Processing Note",
"type": "n8n-nodes-base.stickyNote",
"typeVersion": 1,
"position": [
400,
272
]
},
{
"parameters": {
"httpMethod": "POST",
"path": "llm-usage-webhook",
"options": {}
},
"id": "96c47741-8045-450f-a388-b935a47135a7",
"name": "LLM Usage Webhook",
"type": "n8n-nodes-base.webhook",
"typeVersion": 2,
"position": [
80,
368
]
},
{
"parameters": {
"jsCode": "// Processar dados recebidos via webhook\nconst item = $input.first();\nconst data = item.json.body || item.json;\n\n// Estrutura esperada do webhook:\n// {\n// workflow_id: string,\n// workflow_name: string,\n// run_id: string,\n// node_name: string,\n// provider: string,\n// model: string,\n// usage: {\n// prompt_tokens: number,\n// completion_tokens: number,\n// total_tokens: number\n// },\n// latency_ms: number\n// }\n\nconst processedData = {\n ts: new Date().toISOString(),\n workflow_id: data.workflow_id || $workflow.id,\n workflow_name: data.workflow_name || $workflow.name,\n run_id: data.run_id || $runId,\n node_name: data.node_name || 'Unknown',\n provider: data.provider || 'openai',\n model: data.model || 'gpt-4o-mini',\n input_tokens: data.usage?.prompt_tokens || data.usage?.input_tokens || 0,\n output_tokens: data.usage?.completion_tokens || data.usage?.output_tokens || 0,\n total_tokens: data.usage?.total_tokens || ((data.usage?.prompt_tokens || 0) + (data.usage?.completion_tokens || 0)),\n latency_ms: data.latency_ms || 0,\n status: data.status || 'success',\n user_id: data.user_id || 'webhook'\n};\n\nreturn [{ json: processedData }];"
},
"id": "25203516-397d-432e-9879-bc0731b1cb97",
"name": "Process Webhook Data",
"type": "n8n-nodes-base.code",
"typeVersion": 2,
"position": [
288,
368
]
},
{
"parameters": {
"operation": "executeQuery",
"query": "-- Query para relat\u00f3rio de custo por workflow (\u00faltimos 7 dias)\nSELECT \n workflow_name,\n COUNT(*) as executions,\n SUM(total_tokens) as total_tokens,\n AVG(total_tokens) as avg_tokens_per_run,\n SUM(total_cost_usd) as total_cost_usd,\n AVG(total_cost_usd) as avg_cost_per_run,\n AVG(usd_per_k_token) as avg_usd_per_k_token\nFROM v_llm_usage_cost\nWHERE ts >= NOW() - INTERVAL '7 days'\nGROUP BY workflow_name\nORDER BY total_cost_usd DESC;",
"options": {}
},
"id": "699b9255-c80a-4b3d-b92f-2824c4d2a4bb",
"name": "Generate Weekly Report",
"type": "n8n-nodes-base.postgres",
"typeVersion": 2.5,
"position": [
1088,
160
]
},
{
"parameters": {
"assignments": {
"assignments": [
{
"id": "grafana-queries",
"name": "grafana_queries",
"value": "{\n \"custo_por_fluxo_diario\": \"SELECT date_trunc('day', ts) AS day, workflow_name, SUM(total_cost_usd) AS cost_usd FROM v_llm_usage_cost WHERE $__timeFilter(ts) GROUP BY 1,2 ORDER BY 1 ASC\",\n \"tokens_media_diaria\": \"SELECT date_trunc('day', ts) AS day, AVG(total_tokens) AS avg_tokens_per_run FROM llm_usage WHERE $__timeFilter(ts) GROUP BY 1 ORDER BY 1 ASC\",\n \"custo_semanal\": \"SELECT date_trunc('week', ts) AS bucket, SUM(total_cost_usd) AS cost_usd FROM v_llm_usage_cost WHERE $__timeFilter(ts) GROUP BY 1 ORDER BY 1\",\n \"custo_mensal\": \"SELECT date_trunc('month', ts) AS bucket, SUM(total_cost_usd) AS cost_usd FROM v_llm_usage_cost WHERE $__timeFilter(ts) GROUP BY 1 ORDER BY 1\",\n \"top_workflows_caros\": \"SELECT workflow_name, SUM(total_cost_usd) AS cost_7d FROM v_llm_usage_cost WHERE ts >= NOW() - INTERVAL '7 days' GROUP BY 1 ORDER BY cost_7d DESC LIMIT 10\",\n \"custo_por_1k_tokens\": \"SELECT date_trunc('day', ts) AS day, provider || '|' || model AS model_key, CASE WHEN SUM(total_tokens) = 0 THEN 0 ELSE SUM(total_cost_usd) / (SUM(total_tokens)/1000.0) END AS usd_per_k_token FROM v_llm_usage_cost WHERE $__timeFilter(ts) GROUP BY 1,2 ORDER BY 1\",\n \"kpis_30d\": \"SELECT SUM(total_cost_usd) as total_cost_30d, SUM(total_tokens) as total_tokens_30d, AVG(usd_per_k_token) as avg_cost_per_k_token_30d, COUNT(*) as total_executions_30d FROM v_llm_usage_cost WHERE ts >= NOW() - INTERVAL '30 days'\"\n}",
"type": "object"
}
]
},
"options": {}
},
"id": "553b19e7-7857-4253-806a-4c59d8d1f22f",
"name": "Grafana Queries Reference",
"type": "n8n-nodes-base.set",
"typeVersion": 3.4,
"position": [
1280,
160
]
},
{
"parameters": {
"content": "## Exemplo de N\u00f3 para Adicionar nos Workflows\n\nAdicione este n\u00f3 HTTP Request ap\u00f3s seus n\u00f3s de LLM:",
"height": 80,
"width": 600,
"color": 6
},
"id": "a3f1cf19-0e65-4d82-b984-b653d5fd9423",
"name": "Example Usage Note",
"type": "n8n-nodes-base.stickyNote",
"typeVersion": 1,
"position": [
1488,
64
]
},
{
"parameters": {
"url": "={{ $('LLM Usage Webhook').json.webhookUrl || 'https://sua-instancia-n8n.com/webhook/llm-usage-webhook' }}",
"sendHeaders": true,
"headerParameters": {
"parameters": [
{
"name": "Content-Type",
"value": "application/json"
}
]
},
"sendBody": true,
"bodyParameters": {
"parameters": [
{
"name": "workflow_id",
"value": "={{ $workflow.id }}"
},
{
"name": "workflow_name",
"value": "={{ $workflow.name }}"
},
{
"name": "run_id",
"value": "={{ $runId }}"
},
{
"name": "node_name",
"value": "OpenAI Chat Model"
},
{
"name": "provider",
"value": "openai"
},
{
"name": "model",
"value": "gpt-4o-mini"
},
{
"name": "input_tokens",
"value": "={{ $('OpenAI Chat Model').json.usage?.prompt_tokens || 0 }}"
},
{
"name": "output_tokens",
"value": "={{ $('OpenAI Chat Model').json.usage?.completion_tokens || 0 }}"
},
{
"name": "total_tokens",
"value": "={{ $('OpenAI Chat Model').json.usage?.total_tokens || 0 }}"
},
{
"name": "latency_ms",
"value": "={{ $('OpenAI Chat Model').executionTime || 0 }}"
},
{
"name": "status",
"value": "success"
},
{
"name": "user_id",
"value": "={{ $('Vari\u00c3\u00a1veis')?.json?.mobile || 'system' }}"
}
]
},
"options": {
"response": {
"response": {
"neverError": true
}
}
}
},
"id": "6d4d5a23-bd24-4924-b8e6-6a26e94718ec",
"name": "Monitor LLM Usage",
"type": "n8n-nodes-base.httpRequest",
"typeVersion": 4.2,
"position": [
1488,
160
],
"continueOnFail": true
}
],
"repo_name": "backup-n8n",
"repo_owner": "faelsou",
"repo_path": "backups/xFOifn4m31oXP9Ji",
"settings": {
"executionOrder": "v1"
},
"staticData": null,
"tags": [],
"triggerCount": 0,
"updatedAt": "2025-09-14T01:52:18.680Z",
"versionId": "bbd5cf53-b4cf-402f-88f6-3be8f03d283e"
}
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About this workflow
My workflow 69. Uses n8nTrigger, postgres, httpRequest. Event-driven trigger; 17 nodes.
Source: https://github.com/faelsou/backup-n8n/blob/1a12f60c21e60788cc887b5daca515ad72ca62a4/2025/09/xFOifn4m31oXP9Ji.json — original creator credit. Request a take-down →
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