{"workflow":{"id":14003,"name":"Generate daily multi-cloud FinOps cost and carbon reports with OpenAI GPT-4o","views":89,"recentViews":1,"totalViews":89,"createdAt":"2026-03-11T14:33:33.961Z","description":"\n\n## How It Works\nThis workflow automates multi-cloud billing analysis and FinOps reporting using a supervised multi-agent AI architecture. It targets cloud finance teams, FinOps practitioners, DevOps leads, and CTOs seeking continuous visibility into cloud spend, resource waste, and carbon impact. A daily trigger fetches billing exports via HTTP and parses CSV data. A central Multi-Cloud Optimisation supervisor agent then coordinates four specialised sub-agents: a Resource Utilisation Analyser that identifies idle and over-provisioned assets, a Cost Optimisation Agent that surfaces savings opportunities across providers, a Carbon Footprint Analysis Agent that quantifies emissions per workload, and a FinOps Narrative Generator that produces human-readable financial commentary. Shared tools including a Financial Calculator and Advanced Analytics Code Tool support cross-agent computation. Results are parsed through a Structured Output Parser and formatted into a final consolidated report for stakeholder distribution.\n\n## Setup Steps\n1. Configure the HTTP GET node with your cloud provider.\n2. Connect OpenAI credentials to all four sub-agent model nodes.\n3. Link the Financial Calculator and Advanced Analytics Code Tool nodes  \n4. Configure the Structured Output Parser schema to match your reporting fields.\n5. Test end-to-end with a sample CSV billing export before activating the daily schedule.\n## Prerequisites\n- Cloud provider billing export URLs (AWS, GCP, Azure)\n- n8n instance (v1.0+)\n- HTTP access to billing APIs\n- Report destination (email, Slack, or storage) configured\n## Use Cases\n- FinOps teams generating daily multi-cloud spend digests\n## Customisation\n- Add a Slack or email node to distribute the final report automatically\n## Benefits\n- Daily automation eliminates manual billing export and analysis effort\n","workflow":{"id":"LnX4VjTeIGmWRXud","meta":{"instanceId":"b91e510ebae4127f953fd2f5f8d40d58ca1e71c746d4500c12ae86aad04c1502"},"name":"Multi-Cloud FinOps Cost and Performance Optimization Engine","tags":[],"nodes":[{"id":"2ee6e7ff-7867-495b-a4fd-f786b21f964b","name":"Daily Cost Analysis Trigger","type":"n8n-nodes-base.scheduleTrigger","position":[256,304],"parameters":{"rule":{"interval":[{"triggerAtHour":2}]}},"typeVersion":1.3},{"id":"503953c9-badd-4ad2-8d08-0a15e10c5027","name":"Fetch Billing Exports","type":"n8n-nodes-base.httpRequest","position":[480,304],"parameters":{"url":"<__PLACEHOLDER_VALUE__internal_billing_export_endpoint__>","options":{"response":{"response":{"responseFormat":"file"}}}},"typeVersion":4.4},{"id":"c57cb3ef-4c3a-4a6c-8dbc-4fea81f5fee2","name":"Parse Billing Data","type":"n8n-nodes-base.extractFromFile","position":[704,304],"parameters":{"options":{}},"typeVersion":1.1},{"id":"745b7f91-e162-47c6-8901-67aef3de87cc","name":"Multi-Cloud Optimization Orchestrator","type":"@n8n/n8n-nodes-langchain.agent","position":[1648,304],"parameters":{"text":"={{ $json }}","options":{"systemMessage":"You are a Multi-Cloud Cost Optimization Orchestrator coordinating specialized AI agents to analyze billing data across Azure, AWS, and GCP. Your role is to delegate tasks to the Resource Utilization Analyzer, Cost Optimization Agent, Carbon Footprint Analyzer, and FinOps Narrative Generator. Coordinate their work to produce comprehensive cost optimization insights."},"hasOutputParser":true},"typeVersion":3.1},{"id":"c9093f06-8f4b-4147-9e88-fdc819e1acfc","name":"Orchestrator Model","type":"@n8n/n8n-nodes-langchain.lmChatOpenAi","position":[928,528],"parameters":{"model":{"__rl":true,"mode":"id","value":"gpt-4o"},"options":{"temperature":0.2},"builtInTools":{}},"credentials":{"openAiApi":{"id":"mv2ECvRtbAK63G2g","name":"OpenAi account"}},"typeVersion":1.3},{"id":"c298c385-2fa4-40e3-a9c7-847294dd6566","name":"Resource Utilization Analyzer Agent","type":"@n8n/n8n-nodes-langchain.agentTool","position":[1056,528],"parameters":{"text":"={{ $fromAI('billing_data', 'Billing exports and utilization logs to analyze') }}","options":{"systemMessage":"You are a Resource Utilization Analyzer specializing in multi-cloud infrastructure analysis. Analyze billing data and utilization logs to identify: 1) Over-provisioned resources (CPU/memory allocation vs actual usage), 2) Underutilized clusters and instances, 3) Idle or zombie resources, 4) Right-sizing opportunities, 5) Workload patterns and peak usage times. Provide specific metrics including utilization percentages, waste estimates, and actionable recommendations for each cloud provider (Azure, AWS, GCP)."},"toolDescription":"Analyzes billing exports and utilization logs across Azure, AWS, and GCP to detect over-provisioning, underutilized clusters, idle resources, and optimization opportunities. Returns detailed utilization metrics and waste identification."},"typeVersion":3},{"id":"5353def3-fa1c-4350-a583-fb9ecf7caee7","name":"Utilization Analyzer Model","type":"@n8n/n8n-nodes-langchain.lmChatOpenAi","position":[1136,736],"parameters":{"model":{"__rl":true,"mode":"id","value":"gpt-4o"},"options":{"temperature":0.1},"builtInTools":{}},"credentials":{"openAiApi":{"id":"mv2ECvRtbAK63G2g","name":"OpenAi account"}},"typeVersion":1.3},{"id":"56951abd-6942-4ce6-aa89-2232ea83295c","name":"Cost Optimization Agent","type":"@n8n/n8n-nodes-langchain.agentTool","position":[1344,528],"parameters":{"text":"={{ $fromAI('utilization_findings', 'Resource utilization analysis results to optimize') }}","options":{"systemMessage":"You are a Cost Optimization Strategist specializing in cloud financial planning. Based on utilization analysis, perform: 1) Predictive cost modeling for 6-month trajectories using historical trends, 2) Reserved instance vs on-demand strategy recommendations with ROI calculations, 3) Commitment-based discount opportunities (Savings Plans, Reserved Instances), 4) Carbon footprint estimation based on resource types and regions, 5) Cost-benefit analysis for right-sizing and migration strategies. Provide specific dollar amounts, percentage savings, payback periods, and environmental impact metrics."},"toolDescription":"Performs predictive cost modeling for 6-month trajectories, recommends reserved vs on-demand strategies, estimates carbon footprint implications, and provides financial optimization strategies based on utilization analysis."},"typeVersion":3},{"id":"fd9ce795-1340-404b-a327-4fd956ce36a5","name":"Cost Optimization Model","type":"@n8n/n8n-nodes-langchain.lmChatOpenAi","position":[1424,736],"parameters":{"model":{"__rl":true,"mode":"id","value":"gpt-4o"},"options":{"temperature":0.2},"builtInTools":{}},"credentials":{"openAiApi":{"id":"mv2ECvRtbAK63G2g","name":"OpenAi account"}},"typeVersion":1.3},{"id":"d0c7c468-5f0d-4fcd-98e8-a2151551f0f2","name":"Carbon Footprint Analysis Agent","type":"@n8n/n8n-nodes-langchain.agentTool","position":[1632,528],"parameters":{"text":"={{ $fromAI('resource_data', 'Resource utilization data for carbon footprint analysis') }}","options":{"systemMessage":"You are a Carbon Footprint Analyst specializing in cloud infrastructure sustainability. Based on resource utilization data, calculate: 1) Estimated CO2 emissions by cloud provider and region (using carbon intensity factors), 2) Renewable energy percentage by data center location, 3) Carbon reduction opportunities through region migration, 4) Environmental impact of right-sizing and optimization recommendations, 5) Sustainability metrics aligned with corporate ESG goals. Provide specific CO2 tonnage estimates, percentage improvements, and actionable carbon reduction strategies."},"toolDescription":"Calculates environmental impact metrics and sustainability recommendations based on cloud resource utilization patterns, including CO2 emissions, renewable energy usage, and carbon reduction strategies."},"typeVersion":3},{"id":"cd754a97-9f60-44c0-96d7-83b4a264820e","name":"Carbon Analysis Model","type":"@n8n/n8n-nodes-langchain.lmChatOpenAi","position":[1712,736],"parameters":{"model":{"__rl":true,"mode":"id","value":"gpt-4o"},"options":{"temperature":0.1},"builtInTools":{}},"credentials":{"openAiApi":{"id":"mv2ECvRtbAK63G2g","name":"OpenAi account"}},"typeVersion":1.3},{"id":"9fe523a9-9297-4e88-820b-625facd072ec","name":"FinOps Narrative Generator Agent","type":"@n8n/n8n-nodes-langchain.agentTool","position":[1920,528],"parameters":{"text":"={{ $fromAI('optimization_findings', 'All optimization analysis results to synthesize into narrative') }}","options":{"systemMessage":"You are a FinOps Executive Communications Specialist. Synthesize technical optimization findings into board-ready financial narratives. Create: 1) Executive Summary with key financial metrics and recommendations, 2) Current State Assessment with cost breakdown by cloud provider, 3) Optimization Opportunities with projected savings and ROI, 4) Risk Analysis and implementation considerations, 5) 6-month financial roadmap with milestones, 6) Environmental impact summary for ESG reporting. Use clear business language, quantify all recommendations with dollar amounts and percentages, and structure content for C-level consumption with visual data points."},"toolDescription":"Synthesizes all optimization findings into a board-ready financial narrative with executive summaries, ROI projections, risk assessments, and actionable recommendations formatted for C-level stakeholders."},"typeVersion":3},{"id":"e461d76c-117d-4d29-9cf1-3f56aa1fe165","name":"FinOps Narrative Model","type":"@n8n/n8n-nodes-langchain.lmChatOpenAi","position":[2000,736],"parameters":{"model":{"__rl":true,"mode":"id","value":"gpt-4o"},"options":{"temperature":0.3},"builtInTools":{}},"credentials":{"openAiApi":{"id":"mv2ECvRtbAK63G2g","name":"OpenAi account"}},"typeVersion":1.3},{"id":"596548e6-30d9-4d29-99c4-66047070a384","name":"Financial Calculator","type":"@n8n/n8n-nodes-langchain.toolCalculator","position":[2208,528],"parameters":{},"typeVersion":1},{"id":"92da666d-5906-43ec-b219-6566d9a14a61","name":"Advanced Analytics Code Tool","type":"@n8n/n8n-nodes-langchain.toolCode","position":[2432,528],"parameters":{"language":"python","description":"Performs complex financial modeling, statistical analysis, trend forecasting, and data transformations for cost optimization calculations including compound growth rates, regression analysis, and scenario modeling."},"typeVersion":1.3},{"id":"db698c02-47b2-4065-a49b-79fb06c2fa77","name":"Structured Output Parser","type":"@n8n/n8n-nodes-langchain.outputParserStructured","position":[2624,528],"parameters":{"schemaType":"manual","inputSchema":"{\n  \"type\": \"object\",\n  \"properties\": {\n    \"executive_summary\": {\n      \"type\": \"string\",\n      \"description\": \"High-level overview for C-level stakeholders\"\n    },\n    \"current_state\": {\n      \"type\": \"object\",\n      \"properties\": {\n        \"total_monthly_cost\": {\"type\": \"number\"},\n        \"cost_by_provider\": {\n          \"type\": \"object\",\n          \"properties\": {\n            \"azure\": {\"type\": \"number\"},\n            \"aws\": {\"type\": \"number\"},\n            \"gcp\": {\"type\": \"number\"}\n          }\n        },\n        \"utilization_metrics\": {\n          \"type\": \"object\",\n          \"properties\": {\n            \"average_cpu_utilization\": {\"type\": \"number\"},\n            \"average_memory_utilization\": {\"type\": \"number\"},\n            \"idle_resources_count\": {\"type\": \"number\"}\n          }\n        }\n      }\n    },\n    \"optimization_opportunities\": {\n      \"type\": \"array\",\n      \"items\": {\n        \"type\": \"object\",\n        \"properties\": {\n          \"category\": {\"type\": \"string\"},\n          \"description\": {\"type\": \"string\"},\n          \"estimated_monthly_savings\": {\"type\": \"number\"},\n          \"implementation_effort\": {\"type\": \"string\"},\n          \"priority\": {\"type\": \"string\"}\n        }\n      }\n    },\n    \"six_month_projection\": {\n      \"type\": \"object\",\n      \"properties\": {\n        \"baseline_cost\": {\"type\": \"number\"},\n        \"optimized_cost\": {\"type\": \"number\"},\n        \"total_savings\": {\"type\": \"number\"},\n        \"savings_percentage\": {\"type\": \"number\"},\n        \"roi_months\": {\"type\": \"number\"}\n      }\n    },\n    \"reserved_vs_ondemand_strategy\": {\n      \"type\": \"object\",\n      \"properties\": {\n        \"recommended_reserved_percentage\": {\"type\": \"number\"},\n        \"estimated_ri_savings\": {\"type\": \"number\"},\n        \"payback_period_months\": {\"type\": \"number\"}\n      }\n    },\n    \"carbon_footprint\": {\n      \"type\": \"object\",\n      \"properties\": {\n        \"current_co2_tons_monthly\": {\"type\": \"number\"},\n        \"optimized_co2_tons_monthly\": {\"type\": \"number\"},\n        \"carbon_reduction_percentage\": {\"type\": \"number\"},\n        \"sustainability_recommendations\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}\n      }\n    },\n    \"action_items\": {\n      \"type\": \"array\",\n      \"items\": {\n        \"type\": \"object\",\n        \"properties\": {\n          \"action\": {\"type\": \"string\"},\n          \"owner\": {\"type\": \"string\"},\n          \"timeline\": {\"type\": \"string\"},\n          \"expected_impact\": {\"type\": \"string\"}\n        }\n      }\n    }\n  },\n  \"required\": [\"executive_summary\", \"current_state\", \"optimization_opportunities\", \"six_month_projection\"]\n}"},"typeVersion":1.3},{"id":"d75e4fa0-72b2-4d5f-bea8-ca3b6e6c3b08","name":"Format Final Report","type":"n8n-nodes-base.set","position":[2672,304],"parameters":{"options":{},"assignments":{"assignments":[{"id":"id-1","name":"report_generated_at","type":"string","value":"={{ $now.toISO() }}"},{"id":"id-2","name":"report_type","type":"string","value":"Multi-Cloud Cost Optimization Analysis"},{"id":"id-3","name":"analysis_period","type":"string","value":"Last 30 Days"},{"id":"id-4","name":"optimization_data","type":"object","value":"={{ $json.output }}"},{"id":"id-5","name":"status","type":"string","value":"Complete"}]}},"typeVersion":3.4},{"id":"77284da3-612f-4c93-be27-477b55f80e7e","name":"Sticky Note","type":"n8n-nodes-base.stickyNote","position":[1344,496],"parameters":{"color":7,"width":1488,"height":576,"content":"\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n## Cost Optimisation, Carbon Footprint Analysis, and FinOps Narrative Generator Agents\n\n**What:** Identify savings and rightsizing actions, quantify emissions per workload/provider, and generate financial summaries with recommendations.\n**Why:** Reduce costs, support ESG reporting, and convert complex data into stakeholder-ready insights.\n"},"typeVersion":1},{"id":"77afad8c-2933-492e-bdd6-5eec79249f17","name":"Sticky Note1","type":"n8n-nodes-base.stickyNote","position":[848,496],"parameters":{"color":7,"width":480,"height":512,"content":"\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n## Resource Utilisation Analyser Agent\n**What:** Detects underused and idle cloud resources.\n**Why:** Directly targets the largest source of cloud waste."},"typeVersion":1},{"id":"bd67edce-82a7-4d32-b37a-1e894df63a6c","name":"Sticky Note2","type":"n8n-nodes-base.stickyNote","position":[864,176],"parameters":{"color":7,"width":1696,"height":256,"content":"## Multi-Cloud Optimisation Supervisor\n**What:** Orchestrates four sub-agents with shared tools and memory.\n**Why:** Coordinates parallel analysis for comprehensive coverage."},"typeVersion":1},{"id":"61adbf79-9d62-47ee-8f8d-02d8f4fc2626","name":"Sticky Note3","type":"n8n-nodes-base.stickyNote","position":[208,176],"parameters":{"color":7,"width":624,"height":480,"content":"## Fetch Billing Exports\n**What:** Retrieves cloud billing data via HTTP GET.\n**Why:** Centralises multi-provider cost data at source."},"typeVersion":1},{"id":"e431781f-a4dd-479e-8557-4e5e2ee4dbdf","name":"Sticky Note4","type":"n8n-nodes-base.stickyNote","position":[1344,-224],"parameters":{"color":6,"width":496,"height":368,"content":"## Prerequisites\n- Cloud provider billing export URLs (AWS, GCP, Azure)\n- n8n instance (v1.0+)\n- HTTP access to billing APIs\n- Report destination (email, Slack, or storage) configured\n## Use Cases\n- FinOps teams generating daily multi-cloud spend digests\n## Customisation\n- Add a Slack or email node to distribute the final report automatically\n## Benefits\n- Daily automation eliminates manual billing export and analysis effort"},"typeVersion":1},{"id":"3cbf92dc-0320-40e6-b4ff-76b91d6b1bc9","name":"Sticky Note5","type":"n8n-nodes-base.stickyNote","position":[944,-128],"parameters":{"width":352,"height":272,"content":"## Setup Steps\n1. Configure the HTTP GET node with your cloud provider.\n2. Connect OpenAI credentials to all four sub-agent model nodes.\n3. Link the Financial Calculator and Advanced Analytics Code Tool nodes  \n4. Configure the Structured Output Parser schema to match your reporting fields.\n5. Test end-to-end with a sample CSV billing export before activating the daily schedule."},"typeVersion":1},{"id":"193d8568-2b6f-4083-aa50-151d2b3044ac","name":"Sticky Note6","type":"n8n-nodes-base.stickyNote","position":[224,-144],"parameters":{"width":656,"height":272,"content":"## How It Works\nThis workflow automates multi-cloud billing analysis and FinOps reporting using a supervised multi-agent AI architecture. It targets cloud finance teams, FinOps practitioners, DevOps leads, and CTOs seeking continuous visibility into cloud spend, resource waste, and carbon impact. A daily trigger fetches billing exports via HTTP and parses CSV data. A central Multi-Cloud Optimisation supervisor agent then coordinates four specialised sub-agents: a Resource Utilisation Analyser that identifies idle and over-provisioned assets, a Cost Optimisation Agent that surfaces savings opportunities across providers, a Carbon Footprint Analysis Agent that quantifies emissions per workload, and a FinOps Narrative Generator that produces human-readable financial commentary. Shared tools including a Financial Calculator and Advanced Analytics Code Tool support cross-agent computation. Results are parsed through a Structured Output Parser and formatted into a final consolidated report for stakeholder distribution"},"typeVersion":1},{"id":"3f296e2e-d33f-4e77-b37b-fca4a6de4a76","name":"Sticky Note7","type":"n8n-nodes-base.stickyNote","position":[2608,144],"parameters":{"color":7,"width":352,"height":320,"content":"## Format Final Report\n**What:** Structures all agent outputs into a unified report.\n**Why:** Delivers a clean, consistent deliverable for distribution."},"typeVersion":1}],"active":false,"pinData":{},"settings":{"binaryMode":"separate","availableInMCP":false,"executionOrder":"v1"},"versionId":"78752ca7-fa71-4805-92a5-9a3585b47c78","connections":{"Orchestrator Model":{"ai_languageModel":[[{"node":"Multi-Cloud Optimization Orchestrator","type":"ai_languageModel","index":0}]]},"Parse Billing Data":{"main":[[{"node":"Multi-Cloud Optimization Orchestrator","type":"main","index":0}]]},"Financial Calculator":{"ai_tool":[[{"node":"Multi-Cloud Optimization Orchestrator","type":"ai_tool","index":0}]]},"Carbon Analysis Model":{"ai_languageModel":[[{"node":"Carbon Footprint Analysis Agent","type":"ai_languageModel","index":0}]]},"Fetch Billing Exports":{"main":[[{"node":"Parse Billing Data","type":"main","index":0}]]},"FinOps Narrative Model":{"ai_languageModel":[[{"node":"FinOps Narrative Generator Agent","type":"ai_languageModel","index":0}]]},"Cost Optimization Agent":{"ai_tool":[[{"node":"Multi-Cloud Optimization Orchestrator","type":"ai_tool","index":0}]]},"Cost Optimization Model":{"ai_languageModel":[[{"node":"Cost Optimization Agent","type":"ai_languageModel","index":0}]]},"Structured Output Parser":{"ai_outputParser":[[{"node":"Multi-Cloud Optimization Orchestrator","type":"ai_outputParser","index":0}]]},"Utilization Analyzer Model":{"ai_languageModel":[[{"node":"Resource Utilization Analyzer Agent","type":"ai_languageModel","index":0}]]},"Daily Cost Analysis Trigger":{"main":[[{"node":"Fetch Billing Exports","type":"main","index":0}]]},"Advanced Analytics Code Tool":{"ai_tool":[[{"node":"Multi-Cloud Optimization Orchestrator","type":"ai_tool","index":0}]]},"Carbon Footprint Analysis Agent":{"ai_tool":[[{"node":"Multi-Cloud Optimization Orchestrator","type":"ai_tool","index":0}]]},"FinOps Narrative Generator Agent":{"ai_tool":[[{"node":"Multi-Cloud Optimization Orchestrator","type":"ai_tool","index":0}]]},"Resource Utilization Analyzer Agent":{"ai_tool":[[{"node":"Multi-Cloud Optimization Orchestrator","type":"ai_tool","index":0}]]},"Multi-Cloud Optimization Orchestrator":{"main":[[{"node":"Format Final Report","type":"main","index":0}]]}}},"lastUpdatedBy":1,"workflowInfo":{"nodeCount":25,"nodeTypes":{"n8n-nodes-base.set":{"count":1},"n8n-nodes-base.stickyNote":{"count":8},"n8n-nodes-base.httpRequest":{"count":1},"@n8n/n8n-nodes-langchain.agent":{"count":1},"n8n-nodes-base.extractFromFile":{"count":1},"n8n-nodes-base.scheduleTrigger":{"count":1},"@n8n/n8n-nodes-langchain.toolCode":{"count":1},"@n8n/n8n-nodes-langchain.agentTool":{"count":4},"@n8n/n8n-nodes-langchain.lmChatOpenAi":{"count":5},"@n8n/n8n-nodes-langchain.toolCalculator":{"count":1},"@n8n/n8n-nodes-langchain.outputParserStructured":{"count":1}}},"status":"published","readyToDemo":null,"user":{"name":"Cheng Siong Chin","username":"cschin","bio":"Dr. Cheng Siong CHIN is an n8n workflow creator specializing in AI-powered automation, agent orchestration, and intelligent system integrations. 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