News and updates from FinOps X 2024: How Microsoft is empowering organizations
Last year, I shared a broad set of updates that showcased how Microsoft is embracing FinOps practitioners through education, product improvements, and innovative solutions that help organizations achieve more. with AI-powered experiences like Copilot and Microsoft Fabric. Whether you’re an engineer working in the Azure portal or part of a business or finance team collaborating in Microsoft 365 or analyzing data in Power BI, Microsoft Cloud has the tools you need to accelerate business value for your cloud investments.
A practitioner's guide to accelerating FinOps with GitHub Copilot and FinOps hubs
ℹ️ Quick implementation overview Setup time: ~30 minutes for basic configuration Target audience: FinOps practitioners, finance teams, engineering managers Prerequisites: Azure subscription with FinOps hubs deployed, VS Code, GitHub Copilot Key enabler: FinOps Hub Copilot v0.11 release Key benefits 🎯 Democratized analytics Non-technical team members can perform advanced cost analysis without KQL expertise. ⚡ Faster insights Natural language eliminates query writing overhead and accelerates time-to-insights. 📋 FinOps Framework alignment All queries map directly to validated FinOps Framework capabilities. 🔒 Enterprise ready Built on proven FinOps hub data foundation with security and governance controls. FinOps practitioners face a common challenge: bridging the gap between complex cost data and actionable business insights. While FinOps hubs provide a comprehensive, analytics-ready foundation aligned with the FinOps Framework, accessing and analyzing this data traditionally requires deep technical expertise in KQL and schema knowledge. This guide demonstrates how to perform sophisticated cost analysis using natural language queries using GitHub Copilot in VS Code connected to FinOps hubs 0.11 via the Azure MCP server. This approach democratizes advanced analytics across FinOps teams, supporting faster decision-making and broader organizational adoption of FinOps practices. ℹ️ Understanding the technology stack The Model Context Protocol (MCP) is an open standard that enables AI agents to securely connect to external data sources and tools. The Azure MCP server is Microsoft's implementation that provides this connectivity specifically for Azure resources, while GitHub Copilot acts as the AI agent that translates your natural language questions into the appropriate technical queries. Understanding the foundation: FinOps hubs and natural language integration FinOps hubs serve as the centralized data platform for cloud cost management, providing unified cost and usage data across clouds, accounts, and tenants. The integration with GitHub Copilot through the Azure MCP server introduces a natural language interface that maps practitioner questions directly to validated KQL queries, eliminating the technical barrier that often limits FinOps analysis to specialized team members. Note: The FinOps toolkit also includes Power BI reports, workbooks, alerts, and an optimization engine for advanced analytics and automation. See the FinOps toolkit overview for the full set of capabilities. Key capabilities and technical foundation ℹ️ About the FinOps toolkit ecosystem The FinOps toolkit also includes Power BI reports, workbooks, and an optimization engine for advanced analytics and automation. See the FinOps toolkit overview for the full set of capabilities. FinOps hubs provide several critical capabilities that enable practitioner success: 📊 Data foundation Centralized cost and usage data across multiple cloud providers, billing accounts, and organizational units Native alignment with the FinOps Framework domains and FOCUS specification Analytics-ready data model optimized for performance at scale without complexity overhead 🔗 Integration capabilities Multiple access patterns: Power BI integration, Microsoft Fabric compatibility, and direct KQL access for advanced scenarios Natural language query interface through Azure MCP server integration with Copilot ⚙️ Technical architecture The Azure MCP server acts as the translation layer, implementing the open Model Context Protocol to enable secure communication between AI agents (like GitHub Copilot) and Azure resources. For FinOps scenarios, it specifically provides natural language access to Azure Data Explorer databases containing FinOps hubs data, converting practitioner questions into validated KQL queries while maintaining enterprise authentication and security standards. Mapping FinOps Framework capabilities to natural language queries The integration supports the complete spectrum of FinOps Framework capabilities through natural language interfaces. Each query type maps to specific Framework domains and validated analytical patterns: 💡 Quick reference Each prompt category leverages pre-validated queries from the FinOps hubs query catalog, ensuring consistent, accurate results across different practitioners and use cases. 🔍 Understand phase capabilities Capability Natural language example Business value Cost allocation and accountability "Show me cost allocation by team for Q1" Instant breakdown supporting chargeback discussions Anomaly detection and management "Find any cost anomalies in the last 30 days" Proactive identification of budget risks Reporting and analytics "What are our top resource types by spend?" Data-driven optimization focus areas ⚡ Optimize phase capabilities Capability Natural language example Business value Rate optimization "How much did we save with reservations last month?" Quantification of commitment discount value Workload optimization "Show me underutilized resources" Resource efficiency identification Governance enforcement "Show me resources without proper tags" Policy compliance gaps 📈 Operate phase capabilities Capability Natural language example Business value Forecasting and planning "Forecast next quarter's cloud costs" Proactive budget planning support Performance tracking "Show month-over-month cost trends" Operational efficiency measurement Business value quantification "Calculate our effective savings rate" ROI demonstration for stakeholders Practical implementation: Real-world scenarios and results The following examples demonstrate how natural language queries translate to actionable FinOps insights. Each scenario includes the business context, Framework alignment, query approach, and interpretable results to illustrate the practical value of this integration. ℹ️ Sample data notation All cost figures, dates, and resource names in the following examples are illustrative and provided for demonstration purposes. Actual results will vary based on your organization's Azure usage, billing structure, and FinOps hub configuration. Effective cost allocation and accountability FinOps Framework alignment Domain: Understand usage and cost Capabilities: Allocation, Reporting and analytics Business context Finance teams require accurate cost allocation data to support budget planning and accountability discussions across organizational units. Natural language query What are the top resource groups by cost last month? Query results and business impact The natural language prompt maps to a validated allocation query that aggregates effective cost by resource group, providing the foundational data for chargeback and showback processes. Resource group Effective cost haven $36,972.85 leap $15,613.96 ahbtest $6,824.54 vnet-hub-001 $1,560.13 ... ... 🎯 Key takeaway Natural language queries eliminate the need for complex KQL knowledge while maintaining data accuracy. Finance teams can now perform sophisticated cost allocation analysis without technical barriers. Learn more: Introduction to cost allocation Proactive cost anomaly detection and management FinOps Framework alignment Domain: Understand usage and cost Capabilities: Anomaly management, Reporting and analytics Business context Proactive anomaly detection enables rapid response to unexpected cost changes, supporting budget adherence and operational efficiency. Natural language query Are there any unusual cost spikes or anomalies in the last 12 months? Query results and business impact The system applies time series analysis to identify significant cost deviations, automatically calculating percentage changes and flagging potential anomalies for investigation. Date Daily cost % change vs previous day 2025-06-03 $971.36 -59.54% 2025-06-01 $2,370.16 -4.38% 2025-04-30 $2,302.10 -5.56% 2025-04-02 $2,458.45 +5.79% ... ... ... ⚠️ Warning: Analysis insight The 59% cost reduction on June 3rd indicates a significant operational change, such as workload migration or resource decommissioning, requiring validation to ensure expected behavior. 🎯 Key takeaway Automated anomaly detection enables proactive cost management by identifying unusual spending patterns before they impact budgets, supporting rapid response to operational changes. Learn more: Anomaly management Accurate financial forecasting and budget planning FinOps Framework alignment Domain: Quantify business value Capabilities: Forecasting, Planning and estimating Business context Accurate financial forecasting supports budget planning processes and enables proactive capacity and cost management decisions. Natural language query Forecast total cloud cost for the next 90 days based on the last 12 months. Query results and business impact The forecasting algorithm analyzes historical spending patterns and applies trend analysis to project future costs, providing both daily estimates and aggregate totals for planning purposes. Date Forecasted cost 2025-06-04 $2,401.61 2025-07-01 $2,401.61 2025-08-01 $2,401.61 2025-09-01 $2,401.61 ... ... Total forecasted 90-day spend: $216,145.24 🎯 Key takeaway Natural language forecasting queries provide accurate financial projections based on validated historical analysis, enabling confident budget planning without requiring data science expertise. Learn more: Forecasting Reporting and analytics capabilities FinOps Framework alignment Domain: Understand usage and cost Capabilities: Reporting and analytics Business context Executive reporting requires consistent, reliable cost trend analysis to support strategic decision-making and budget performance tracking. Natural language query Show monthly billed and effective cost trends for the last 12 months. Query results and business impact Month Billed cost Effective cost 2024-06 $46,066.39 $46,773.85 2024-07 $72,951.41 $74,004.08 2024-08 $73,300.31 $74,401.81 2024-09 $71,886.30 $72,951.26 ... ... ... Learn more: Reporting and analytics Resource optimization analysis FinOps Framework alignment Domain: Optimize usage and cost Capabilities: Workload optimization, Reporting and analytics Business context Prioritizing optimization efforts requires understanding which resource types drive the most cost, enabling focused improvement initiatives with maximum business impact. Natural language query What are the top resource types by cost last month? Query results and business impact Resource type Effective cost Fabric Capacity $34,283.52 Virtual machine scale set $15,155.59 SQL database $2,582.99 Virtual machine $2,484.34 ... ... Learn more: Workload optimization Implementation methodology This section provides a systematic approach to implementing natural language FinOps analysis using the technical foundation established above. Prerequisites and environment validation Before proceeding with implementation, ensure you have: ✅ Azure subscription with appropriate FinOps hub deployment permissions ✅ Node.js runtime environment (required by Azure MCP Server) ✅ Visual Studio Code with GitHub Copilot extension ✅ Azure CLI, Azure PowerShell, or Azure Developer CLI authentication configured Access validation methodology Step 1: Verify FinOps hub deployment Confirm hub deployment status and data ingestion through the FinOps hubs setup guide Step 2: Validate database access Test connectivity to the hub database using Azure Data Explorer web application or Azure portal Step 3: Confirm schema availability Verify core functions (Costs, Prices) and databases (Hub, Ingestion) are accessible with current data Expected Database Structure Hub database: Public-facing functions including Costs, Prices, and version-specific functions (e.g., Costs_v1_0) Ingestion database: Raw data tables, configuration settings (HubSettings, HubScopes), and open data tables (PricingUnits) FOCUS-aligned data: All datasets conform to FinOps Open Cost and Usage Specification standards Learn more: FinOps hubs template details Azure MCP server configuration ℹ️ What is Azure MCP Server? The Azure Model Context Protocol (MCP) server is a Microsoft-provided implementation that enables AI agents and clients to interact with Azure resources through natural language commands. It implements the open Model Context Protocol standard to provide secure, structured access to Azure services including Azure Data Explorer (FinOps hub databases). Key capabilities and service support The Azure MCP server provides comprehensive Azure service integration, particularly relevant for FinOps analysis: 🔍 FinOps-relevant services Azure Data Explorer: Execute KQL queries against FinOps hub databases Azure Monitor: Query logs and metrics for cost analysis Resource groups: List and analyze organizational cost structures Subscription management: Access subscription-level cost data 🔧 Additional Azure services Azure Storage, Cosmos DB, Key Vault, Service Bus, and 10+ other services Full list available in the Azure MCP Server tools documentation Installation methodology The Azure MCP Server is available as an NPM package and VS Code extension. For FinOps scenarios, we recommend the VS Code extension approach for seamless integration with GitHub Copilot. Option 1: VS Code extension (recommended) Install the Azure MCP server extension from VS Code Marketplace The extension automatically configures the server in your VS Code settings Open GitHub Copilot and activate Agent Mode to access Azure tools Option 2: Manual configuration Add the following to your MCP client configuration: { "servers": { "Azure MCP Server": { "command": "npx", "args": ["-y", "@azure/mcp@latest", "server", "start"] } } } Authentication requirements Azure MCP Server uses Entra ID through the Azure Identity library, following Azure authentication best practices. It supports: Azure CLI: az login (recommended for development) Azure PowerShell: Connect-AzAccount Azure Developer CLI: azd auth login Managed identity: For production deployments The server uses DefaultAzureCredential and automatically discovers the best available authentication method for your environment. Technical validation steps Step 1: Authentication verification Confirm successful login to supported Azure tools Step 2: Resource discovery Validate MCP Server can access your Azure subscription and FinOps hub resources Step 3: Database connectivity Test query execution against FinOps hub databases Integration with development environment VS Code configuration requirements: GitHub Copilot extension with Agent Mode capability Azure MCP Server installation and configuration FinOps hubs copilot instructions and configuration files The FinOps Hub Copilot v0.11 release provides pre-configured GitHub Copilot instructions specifically tuned for FinOps analysis. This release includes: AI agent instructions optimized for FinOps Framework capabilities GitHub Copilot configuration files for VS Code Agent Mode Validated query patterns mapped to common FinOps scenarios Azure MCP Server integration guides for connecting to FinOps hub data Verification methodology: Open Copilot Chat interface (Ctrl+Shift+I / Cmd+Shift+I) Activate Agent Mode and select tools icon to verify Azure MCP Server availability Execute connectivity test: "What Azure resources do I have access to?" Expected response validation: Successful authentication confirmation Azure subscription and resource enumeration FinOps hub database connectivity status Progressive query validation Foundational test queries: Complexity level Validation query Expected behavior Basic "Show me total cost for last month" Single aggregate value with currency formatting Intermediate "What are my top 10 resource groups by cost?" Tabular results with proper ranking Advanced "Find any costs over $1000 in the last week" Filtered results with anomaly identification Query execution validation: KQL translation accuracy against FinOps hub schema Result set formatting and data type handling Error handling and user feedback mechanisms Operational best practices for enterprise implementation Query optimization and performance considerations Data volume management: Implement temporal filtering to prevent timeout scenarios (Azure Data Explorer 64MB result limit) Use summarization functions for large datasets rather than detailed row-level analysis Apply resource-level filters when analyzing specific environments or subscriptions Schema consistency validation: Reference the FinOps hub database guide for authoritative column definitions Verify data freshness through ingestion timestamp validation Validate currency normalization across multi-subscription environments Query pattern optimization: Leverage the FinOps hub query catalog for validated analytical patterns Customize costs-enriched-base query foundation for organization-specific requirements Implement proper time zone handling for global operational environments Security and access management Authentication patterns: Utilize Azure CLI integrated authentication for development environments Implement service principal authentication for production automation scenarios Maintain principle of least privilege for database access permissions Data governance considerations: Ensure compliance with organizational data classification policies Implement appropriate logging for cost analysis queries and results Validate that natural language prompts don't inadvertently expose sensitive financial data Comprehensive query patterns by analytical domain The following reference provides validated natural language prompts mapped to specific FinOps Framework capabilities and proven KQL implementations. Technical note: Each pattern references validated queries from the FinOps hub query catalog. Verify schema compatibility using the FinOps hub database guide before implementation. Cost visibility and allocation patterns Analytical requirement FinOps Framework alignment Validated natural language query Executive cost trend reporting Reporting and analytics "Show monthly billed and effective cost trends for the last 12 months." Resource group cost ranking Allocation "What are the top resource groups by cost last month?" Quarterly financial reporting Allocation / Reporting and analytics "Show quarterly cost by resource group for the last 3 quarters." Service-level cost analysis Reporting and analytics "Which Azure services drove the most cost last month?" Organizational cost allocation Allocation / Reporting and analytics "Show cost allocation by team and product for last quarter." Optimization and efficiency patterns Analytical requirement FinOps Framework alignment Validated natural language query Resource optimization prioritization Workload optimization "What are the top resource types by cost last month?" Commitment discount analysis Rate optimization "Show reservation recommendations and break-even analysis for our environment." Underutilized resource identification Workload optimization "Find resources with low utilization that could be optimized or decommissioned." Savings plan effectiveness Rate optimization "How much did we save with savings plans compared to pay-as-you-go pricing?" Tag compliance monitoring Data ingestion "Show me resources without required cost center tags." Anomaly detection and monitoring patterns Analytical requirement FinOps Framework alignment Validated natural language query Cost spike identification Anomaly management "Find any unusual cost spikes or anomalies in the last 30 days." Budget variance analysis Budgeting "Show actual vs. budgeted costs by resource group this quarter." Trending analysis Reporting and analytics "Identify resources with consistently increasing costs over the last 6 months." Threshold monitoring Anomaly management "Alert me to any single resources costing more than $5,000 monthly." Governance and compliance patterns Analytical Requirement FinOps Framework Alignment Validated Natural Language Query Policy compliance validation Policy and governance "Show resources that don't comply with our tagging policies." Approved service usage Policy and governance "List any non-approved services being used across our subscriptions." Regional compliance monitoring Policy and governance "Verify all resources are deployed in approved regions only." Cost center accountability Invoicing and chargeback "Generate chargeback reports by cost center for last quarter." Key takeaway: These validated query patterns provide a comprehensive foundation for FinOps analysis across all Framework capabilities. Use them as templates and customize for your organization's specific requirements. Troubleshooting and optimization guidance Common query performance issues ⚠️ Warning: Performance considerations Azure Data Explorer has a 64MB result limit by default. Proper query optimization avoids timeouts and ensures reliable performance. If using Power BI, use DirectQuery to connect to your data. Large dataset timeouts Symptom: Queries failing with timeout errors on large datasets Solution: Add temporal filters ✅ Recommended: "Show costs for last 30 days" ❌ Avoid: "Show all costs" Framework alignment: Data ingestion Memory limit exceptions Symptom: Exceeding Azure Data Explorer 64MB result limit Solution: Use aggregation functions ✅ Recommended: "Summarize costs by month" ❌ Avoid: Daily granular data for large time periods Best practice: Implement progressive drill-down from summary to detail Schema validation errors Symptom: Queries returning empty results or unexpected columns Solution: Verify hub schema version compatibility using the database guide Validation: Test with known queries from the query catalog Query optimization best practices Temporal filtering ✅ Recommended: "Show monthly costs for Q1 2025" ❌ Avoid: "Show all historical costs by day" Aggregation-first approach ✅ Recommended: "Top 10 resource groups by cost" ❌ Avoid: "All resources with individual costs" Multi-subscription handling ✅ Recommended: "Costs by subscription for production environment" ❌ Avoid: "All costs across all subscriptions without filtering" Conclusion The integration of FinOps hubs with natural language querying through GitHub Copilot and Azure MCP Server represents a transformative advancement in cloud financial management accessibility. By eliminating technical barriers traditionally associated with cost analysis, this approach enables broader organizational adoption of FinOps practices while maintaining analytical rigor and data accuracy. Key takeaways for implementation success Foundation building Start with the basics: Ensure robust FinOps hub deployment with clean, consistent data ingestion Validate authentication and connectivity before advancing to complex scenarios Begin with basic queries and progressively increase complexity as team familiarity grows Business value focus Align with organizational needs: Align query patterns with organizational FinOps maturity and immediate business needs Prioritize use cases that demonstrate clear ROI and operational efficiency gains Establish feedback loops with finance and business stakeholders to refine analytical approaches Scale and governance planning Design for enterprise success: Implement appropriate access controls and data governance from the beginning Design query patterns that perform well at organizational scale Establish monitoring and alerting for cost anomalies and policy compliance Future considerations As natural language interfaces continue to evolve, organizations should prepare for enhanced capabilities including: 🔮 Advanced analytics Multi-modal analysis: Integration of cost data with performance metrics, compliance reports, and business KPIs Predictive analytics: Advanced forecasting and scenario modeling through conversational interfaces 🤖 Automated intelligence Automated optimization: Natural language-driven resource rightsizing and commitment recommendations Cross-platform intelligence: Unified analysis across cloud providers, SaaS platforms, and on-premises infrastructure The democratization of FinOps analytics through natural language interfaces positions organizations to make faster, more informed decisions about cloud investments while fostering a culture of cost consciousness across all teams. Success with this integration requires both technical implementation excellence and organizational change management to maximize adoption and business impact. Learn more about the FinOps toolkit and stay updated on new capabilities at the FinOps toolkit website.
Unlock Cost Savings with Azure AI Foundry Provisioned Throughput reservations
In the ever-evolving world of artificial intelligence, businesses are constantly seeking ways to optimize their costs and streamline their operations while leveraging cutting-edge technologies. To help, Microsoft recently announced Azure AI Foundry Provisioned Throughput reservations which provide an innovative solution to achieve both. This offering is coming soon and will enable organizations to save significantly on their AI deployments by committing to specific throughput usage. Here’s a high-level look at what this offer is, how it works, and the benefits it brings. What are Azure AI Foundry Provisioned Throughput reservations? Prior to this announcement, Azure reservations could only apply to AI workloads running Azure OpenAI Service models. These Azure reservations were called “Azure OpenAI Service Provisioned reservations”. Now that more models are available on Azure AI Foundry and Azure reservations can apply to these models, Microsoft launched “Azure AI Foundry Provisioned Throughput reservations”. Azure AI Foundry Provisioned Throughput reservations is a strategic pricing offer for businesses using Provisioned Throughput Units (PTUs) to deploy AI models. Reservations enable businesses to reduce AI workload costs on predictable consumption patterns by locking in significant discounts compared to hourly pay-as-you-go pricing. How It Works The concept is simple yet powerful: instead of paying the PTU hourly rate for your AI model deployments, you pre-purchase a set quantity of PTUs for a specific term—either one month or one year in a specific region and deployment to receive a discounted price. The reservation applies to the deployment type (e.g., Global, Data Zone, or Regional*), and region. Azure AI Foundry Provisioned Throughput reservations are not model dependent, meaning that you do not have to commit to a model when purchasing. For example, if you deploy 3 Global PTUs in East US, you can purchase 3 Global PTU reservations in East US to significantly reduce your costs. It’s important to note that reservations are tied to deployment types and region, meaning a Global reservation won’t apply to Data Zone or Regional deployments and East US reservation won’t apply to West US deployments. Key Benefits Azure AI Foundry Provisioned Throughput reservations offer several benefits that make them an attractive option for organizations: Cost Savings: By committing to a reservation, businesses can save up to 70% compared to hourly pricing***. This makes it an ideal choice for production workloads, large-scale deployments, and steady usage patterns. Budget Control: Reservations are available for one-month or one-year terms, allowing organizations to align costs with their budget goals. Flexible terms ensure businesses can choose what works best for their financial planning. Streamlined Billing: The reservation discount applies automatically to matching deployments, simplifying cost management and ensuring predictable expenditures. How to Purchase a reservation Purchasing an Azure AI Foundry Provisioned Throughput reservation is straightforward: Sign in to the Azure Portal and navigate to the Reservations section. Select the scope you want the reservation to apply to (shared, management group, single subscription, single resource group) Select the deployment type (Global, Data Zone, or Regional) and the Azure region you want to cover. Specify the quantity of PTUs and the term (one month or one year). Add the reservation to your cart and complete the purchase. Reservations can be paid for upfront or through monthly payments, depending on your subscription type. The reservation begins immediately upon purchase and applies to any deployments matching the reservation's attributes. Best Practices Important: Azure reservations are NOT deployments —they are entirely related to billing. The Azure reservation itself doesn’t guarantee capacity, and capacity availability is very dynamic. To maximize the value of your reservation, follow these best practices: Deploy First: Create your deployments before purchasing a reservation to ensure you don’t overcommit to PTUs you may not use. Match Deployment Attributes: Ensure the scope, region, and deployment type of your reservation align with your actual deployments. Plan for Renewal: Reservations can be set to auto-renew, ensuring continuous cost savings without service interruptions. Monitor and manage: Post purchase of reservations it is important to regularly monitor your reservation utilization and setup budget alerts. Exchange reservations: Exchange your reservations if your workloads change throughout your term. Why Choose Azure AI Foundry Provisioned Throughput reservations? Azure AI Foundry Provisioned Throughput reservations are a perfect blend of cost efficiency and flexibility. Whether you’re deploying AI models for real-time processing, large-scale data transformations, or enterprise applications, this offering helps you reduce costs while maintaining high performance. By committing to a reservation, you can not only save money but also streamline your billing and gain better control over your AI expenses. Conclusion As businesses continue to adopt AI technologies, managing costs becomes a critical factor in ensuring scalability and success. Azure AI Foundry Provisioned Throughput reservations empower organizations to achieve their AI goals without breaking the bank. By aligning your workload requirements with this innovative offer, you can unlock significant savings while maintaining the flexibility and capabilities needed to drive innovation. Ready to get started? Learn more about Azure reservations and be on the lookout for Azure AI Foundry Provisioned Throughput reservations to be available to purchase in your Azure portal and get started with Additional Resources: What are Azure Reservations? - Microsoft Cost Management | Microsoft Learn Azure Pricing Overview | Microsoft Azure Azure Essentials | Microsoft Azure Azure AI Foundry | Microsoft Azure *Not all models will be available regionally. **Not all models will be available for Azure AI Foundry Provisioned Throughput reservations. *** The 70% savings is based on the GPT-4o Global provisioned throughput Azure hourly rate of approximately $1/hour, compared to the reduced rate of a 1-year Azure reservation at approximately $0.3027/hour. Azure pricing as of May 1, 2025 (prices subject to change. Actual savings may vary depending on the specific Large Language Model and region availability.)Managing Azure OpenAI costs with the FinOps toolkit and FOCUS: Turning tokens into unit economics
By Robb Dilallo Introduction As organizations rapidly adopt generative AI, Azure OpenAI usage is growing—and so are the complexities of managing its costs. Unlike traditional cloud services billed per compute hour or storage GB, Azure OpenAI charges based on token usage. For FinOps practitioners, this introduces a new frontier: understanding AI unit economics and managing costs where the consumed unit is a token. This article explains how to leverage the Microsoft FinOps toolkit and the FinOps Open Cost and Usage Specification (FOCUS) to gain visibility, allocate costs, and calculate unit economics for Azure OpenAI workloads. Why Azure OpenAI cost management is different AI services break many traditional cost management assumptions: Billed by token usage (input + output tokens). Model choices matter (e.g., GPT-3.5 vs. GPT-4 Turbo vs. GPT-4o). Prompt engineering impacts cost (longer context = more tokens). Bursty usage patterns complicate forecasting. Without proper visibility and unit cost tracking, it's difficult to optimize spend or align costs to business value. Step 1: Get visibility with the FinOps toolkit The Microsoft FinOps toolkit provides pre-built modules and patterns for analyzing Azure cost data. Key tools include: Microsoft Cost Management exports Export daily usage and cost data in a FOCUS-aligned format. FinOps hubs Infrastructure-as-Code solution to ingest, transform, and serve cost data. Power BI templates Pre-built reports conformed to FOCUS for easy analysis. Pro tip: Start by connecting your Microsoft Cost Management exports to a FinOps hub. Then, use the toolkit’s Power BI FOCUS templates to begin reporting. Learn more about the FinOps toolkit Step 2: Normalize data with FOCUS The FinOps Open Cost and Usage Specification (FOCUS) standardizes billing data across providers—including Azure OpenAI. FOCUS Column Purpose Azure Cost Management Field ServiceName Cloud service (e.g., Azure OpenAI Service) ServiceName ConsumedQuantity Number of tokens consumed Quantity PricingUnit Unit type, should align to "tokens" DistinctUnits BilledCost Actual cost billed CostInBillingCurrency ChargeCategory Identifies consumption vs. reservation ChargeType ResourceId Links to specific deployments or apps ResourceId Tags Maps usage to teams, projects, or environments Tags UsageType / Usage Details Further SKU-level detail Sku Meter Subcategory, Sku Meter Name Why it matters: Azure’s native billing schema can vary across services and time. FOCUS ensures consistency and enables cross-cloud comparisons. Tip: If you use custom deployment IDs or user metadata, apply them as tags to improve allocation and unit economics. Review the FOCUS specification Step 3: Calculate unit economics Unit cost per token = BilledCost ÷ ConsumedQuantity Real-world example: Calculating unit cost in Power BI A recent Power BI report breaks down Azure OpenAI usage by: SKU Meter Category → e.g., Azure OpenAI SKU Meter Subcategory → e.g., gpt 4o 0513 Input global Tokens SKU Meter Name → detailed SKU info (input/output, model version, etc.) GPT Model Usage Type Effective Cost gpt 4o 0513 Input global Tokens Input $292.77 gpt 4o 0513 Output global Tokens Output $23.40 Unit Cost Formula: Unit Cost = EffectiveCost ÷ ConsumedQuantity Power BI Measure Example: Unit Cost = SUM(EffectiveCost) / SUM(ConsumedQuantity) Pro tip: Break out input and output token costs by model version to: Track which workloads are driving spend. Benchmark cost per token across GPT models. Attribute costs back to teams or product features using Tags or ResourceId. Power BI tip: Building a GPT cost breakdown matrix To easily calculate token unit costs by GPT model and usage type, build a Matrix visual in Power BI using this hierarchy: Rows: SKU Meter Category SKU Meter Subcategory SKU Meter Name Values: EffectiveCost (sum) ConsumedQuantity (sum) Unit Cost (calculated measure) Unit Cost = SUM(‘Costs’[EffectiveCost]) / SUM(‘Costs’[ConsumedQuantity]) Hierarchy Example: Azure OpenAI ├── GPT 4o Input global Tokens ├── GPT 4o Output global Tokens ├── GPT 4.5 Input global Tokens └── etc. Power BI Matrix visual showing Azure OpenAI token usage and costs by SKU Meter Category, Subcategory, and Name. This breakdown enables calculation of unit cost per token across GPT models and usage types, supporting FinOps allocation and unit economics analysis. What you can see at the token level Metric Description Data Source Token Volume Total tokens consumed Consumed Quantity Effective Cost Actual billed cost BilledCost / Cost Unit Cost per Token Cost divided by token quantity Effective Unit Price SKU Category & Subcategory Model, version, and token type (input/output) Sku Meter Category, Subcategory, Meter Name Resource Group / Business Unit Logical or organizational grouping Resource Group, Business Unit Application Application or workload responsible for usage Application (tag) This visibility allows teams to: Benchmark cost efficiency across GPT models. Track token costs over time. Allocate AI costs to business units or features. Detect usage anomalies and optimize workload design. Tip: Apply consistent tagging (Cost Center, Application, Environment) to Azure OpenAI resources to enhance allocation and unit economics reporting. How the FinOps Foundation’s AI working group informs this approach The FinOps for AI overview, developed by the FinOps Foundation’s AI working group, highlights unique challenges in managing AI-related cloud costs, including: Complex cost drivers (tokens, models, compute hours, data transfer). Cross-functional collaboration between Finance, Engineering, and ML Ops teams. The importance of tracking AI unit economics to connect spend with value. By combining the FinOps toolkit, FOCUS-conformed data, and Power BI reporting, practitioners can implement many of the AI Working Group’s recommendations: Establish token-level unit cost metrics. Allocate costs to teams, models, and AI features. Detect cost anomalies specific to AI usage patterns. Improve forecasting accuracy despite AI workload variability. Tip: Applying consistent tagging to AI workloads (model version, environment, business unit, and experiment ID) significantly improves cost allocation and reporting maturity. Step 4: Allocate and Report Costs With FOCUS + FinOps toolkit: Allocate costs to teams, projects, or business units using Tags, ResourceId, or custom dimensions. Showback/Chargeback AI usage costs to stakeholders. Detect anomalies using the Toolkit’s patterns or integrate with Azure Monitor. Tagging tip: Add metadata to Azure OpenAI deployments for easier allocation and unit cost reporting. Example: tags: CostCenter: AI-Research Environment: Production Feature: Chatbot Step 5: Iterate Using FinOps Best Practices FinOps capability Relevance Reporting & analytics Visualize token costs and trends Allocation Assign costs to teams or workloads Unit economics Track cost per token or business output Forecasting Predict future AI costs Anomaly management Identify unexpected usage spikes Start small (Crawl), expand as you mature (Walk → Run). Learn about the FinOps Framework Next steps Ready to take control of your Azure OpenAI costs? Deploy the Microsoft FinOps toolkit Start ingesting and analyzing your Azure billing data. Get started Adopt FOCUS Normalize your cost data for clarity and cross-cloud consistency. Explore FOCUS Calculate AI unit economics Track token consumption and unit costs using Power BI. Customize Power BI reports Extend toolkit templates to include token-based unit economics. Join the conversation Share insights or questions with the FinOps community on TechCommunity or in the FinOps Foundation Slack. Advance Your Skills Consider the FinOps Certified FOCUS Analyst certification. Further Reading Managing the cost of AI: Understanding AI workload cost considerations Microsoft FinOps toolkit Learn about FOCUS Microsoft Cost Management + Billing FinOps Foundation Appendix: FOCUS column glossary ConsumedQuantity: The number of tokens or units consumed for a given SKU. This is the key measure of usage. ConsumedUnit: The type of unit being consumed, such as 'tokens', 'GB', or 'vCPU hours'. Often appears as 'Units' in Azure exports for OpenAI workloads. PricingUnit: The unit of measure used for pricing. Should match 'ConsumedUnit', e.g., 'tokens'. EffectiveCost: Final cost after amortization of reservations, discounts, and prepaid credits. Often derived from billing data. BilledCost: The invoiced charge before applying commitment discounts or amortization. PricingQuantity: The volume of usage after applying pricing rules such as tiered or block pricing. Used to calculate cost when multiplied by unit price.Convert your Linux workloads while cutting costs with Azure Hybrid Benefit
As organizations increasingly adopt hybrid and cloud-first strategies to accelerate growth, managing costs is a top priority. Azure Hybrid Benefit provides discounts on Windows and SQL server licenses and subscriptions helping organizations reduce expenses during their migration to Azure. But did you know that Azure Hybrid Benefit also extends to Linux? In this blog, we’ll explore how Azure Hybrid Benefit for Linux enables enterprises to modernize their infrastructure, reduce cloud costs, and maintain seamless hybrid operations—all with the flexibility of easily converting their existing Red Hat Enterprise Linux (RHEL) and SUSE Linux Enterprise Server (SLES) subscriptions. We’ll also dig into the differences in entitlements between organizations using Linux, Windows Server, and SQL licenses. Whether you’re migrating workloads or running a hybrid cloud environment, understanding this Azure offer can help you make the most of your subscription investments. Leverage your existing licenses while migrating to Azure Azure Hybrid Benefit for Linux allows organizations to leverage their existing RHEL or SLES licenses to migrate to in Azure, with a cost savings of up to 76% when combined with three-year Azure Reserved Instances. This offering provides significant advantages for businesses looking to migrate their Linux workloads to Azure or optimize their current Azure deployments: Seamless conversion: Existing pay-as-you-go Linux VMs can be converted to bring-your-own-subscription billing without downtime or redeployment Cost reduction: Organizations only pay for VM compute costs, eliminating software licensing fees for eligible Linux VMs Automatic maintenance: Microsoft handles image maintenance, updates, and patches for converted RHEL and SLES images Unified management: It integrates with Azure CLI and provides the same user interface as other Azure VMs Simplified support: Organizations can receive co-located technical support from Azure, Red Hat, and SUSE with a single support ticket To use Azure Hybrid Benefit for Linux, customers must have eligible RedHat or SUSE subscriptions. For RHEL, customers need to enable their RedHat products for Cloud Access on Azure through RedHat Subscription Management before applying the benefit. Minimizing downtime and licensing costs with Azure Hybrid Benefit To illustrate the value of leveraging Azure Hybrid Benefit for Linux, let’s imagine a common use case with a hypothetical business. Contoso, a growing SaaS provider, initially deployed its application on Azure virtual machines (VMs) using a pay-as-you-go model. As demand for its platform increased, Contoso scaled its infrastructure, running a significant number of Linux-based VMs on Azure. With this growth, the company recognized an opportunity to optimize costs by negotiating a better Red Hat subscription directly with the vendor. Instead of restarting or migrating their workloads—an approach that could cause downtime and disrupt their customers' experience—Contoso leveraged Azure Hybrid Benefit for Linux VMs. This allowed them to seamlessly apply their existing Red Hat subscription to their Azure VMs without downtime, reducing licensing costs while maintaining operational stability. By using Azure Hybrid Benefit, Contoso successfully balanced cost savings and scalability while continuing to grow on Azure and provide continuous service to their customers. How does a Linux license differ from Windows or SQL? Entitlements for customers using Azure Hybrid Benefit for Linux are structured differently from those for Windows Server or SQL Server, primarily due to differences in licensing models, workload types, migration strategies, and support requirement. Azure Hybrid Benefit for Windows and SQL Azure Hybrid Benefit for Linux Azure Hybrid Benefit helps organizations reduce expenses during their migration to the cloud by providing discounts on SQL Server and Windows servers licenses with active Software Assurance. Additionally, they benefit from free extended security updates (ESUs) when migrating older Windows Server or SQL Server versions to Azure. Azure Hybrid Benefit for Windows and SQL customers typically manage traditional Windows-based workloads, including Active Directory, .NET applications, AKS, ADH, Azure Local, NC2, AVS, and enterprise databases, often migrating on-premises SQL Server databases to Azure SQL Managed Instance or Azure VMs. Windows and SQL customers frequently execute lift-and-shift migrations from on-premises Windows Server or SQL Server to Azure, often staying within the Microsoft stack. Azure Hybrid Benefit for Linux customers leverage their existing RHEL (Red Hat Enterprise Linux) or SLES (SUSE Linux Enterprise Server) subscriptions, benefiting from bring-your-own-subscription (BYOS) pricing rather than paying for Azure's on-demand rates. They typically work with enterprise Linux vendors for ongoing support. Azure Hybrid Benefit for Linux customers often run enterprise Linux workloads, such as SAP, Kubernetes-based applications, and custom enterprise applications, and are more likely to be DevOps-driven, leveraging containers, open-source tools, and automation frameworks. Linux users tend to adopt modern, cloud-native architectures, focusing on containers (AKS), microservices, and DevOps pipelines, while often implementing hybrid and multi-cloud strategies that integrate Azure with other major cloud providers. In conclusion, Azure Hybrid Benefit is a valuable offer for organizations looking to optimize their cloud strategy and manage costs effectively. By extending this benefit to Linux, Microsoft has opened new avenues for organization to modernize their infrastructure, reduce cloud expenses, and maintain seamless hybrid operations. With the ability to leverage existing RedHat Enterprise Linux (RHEL) and SUSE Linux Enterprise Server (SLES) subscriptions, organizations can enjoy significant cost savings, seamless conversion of pay-as-you-go Linux VMs, automatic maintenance, unified management, and simplified support. Azure Hybrid Benefit for Linux not only provides flexibility and efficiency but also empowers organizations to make the most of their subscription investments while accelerating their growth in a hybrid and cloud-first world. Whether you're migrating workloads or running a hybrid cloud environment, understanding and utilizing this benefit can help you achieve your strategic goals with confidence. To learn more go to: Explore Azure Hybrid Benefit for Linux VMs - Azure Virtual Machines | Microsoft LearnMaximize efficiency by managing and exchanging your Azure OpenAI Service provisioned reservations
When it comes to AI, businesses confront unprecedented challenges in efficiently managing computational resources. That’s why Azure OpenAI Service is a critical platform for organizations seeking to leverage cutting-edge AI capabilities, and it makes provisioned reservations an essential strategy for intelligent cost savings. Business needs change, of course, and flexibility in managing these reservations is vital. In this blog, we’ll not only explore what makes Azure OpenAI Service provisioned reservations indispensable for organizations seeking resilience and cost efficiency in their AI operations, but also follow a fictional company, Contoso, to illustrate real-world scenarios where exchanging reservations enhances scalability and budget control. The crucial role of provisioned reservations in modern AI infrastructure Azure OpenAI Service provisioned reservations help organizations save money by committing to a month- or yearlong provisioned throughput unit reservation for AI model usage, ensuring guaranteed availability and predictable costs. As mentioned in this article, purchasing a reservation and choosing coverage for an Azure region, quantity, and deployment type, reduces costs as compared to being charged at hourly rates. Actively managing and monitoring these reservations is paramount to unlocking their full potential. Here's why: Optimizing utilization: Regular monitoring ensures that your reservations align with your actual usage, preventing wasted resources. Adapting to business changes: As business needs shift, reservations can be adjusted to accommodate evolving requirements. Avoiding over-commitment: Proactive management helps prevent over-purchasing reservations, which can lead to unnecessary expenses. Enhancing cost control and accountability: By tracking reservation usage and costs, organizations can maintain better control over their AI budgets. Leveraging AI usage insights: Analyzing reservation utilization provides valuable insights into AI application performance and usage patterns. The value of exchanging provisioned reservations One of the most powerful aspects of provisioned reservations is the ability to exchange them. This flexibility allows businesses to adapt their commitments to better align with their evolving needs. Exchanges can be initiated through the Azure Portal or via the Azure Reservation API, offering seamless adjustments. Consider Contoso, a global technology firm leveraging Azure OpenAI Service for customer support chatbots and content generation tools. Initially, Contoso’s needs were straightforward, but as their business expanded, their AI requirements changed. This is where the exchange feature proved invaluable. Types of provisioned reservations exchanges Contoso leveraged several types of exchanges to optimize their Azure OpenAI Service usage: Region exchange: Contoso initially committed to a reservation in the East US region. However, as their operations expanded into Western Europe, they needed to shift their AI workloads. By exchanging reservations, they were able to apply their discounted billing to the West Europe region, ensuring optimal performance for their growing user base. Deployment type exchange: There are three types of deployment: Global, Azure geography (or regional), and Microsoft specified data zone. Contoso initially reserved regional deployments for their inference operations, but because of growing demand they switched to global deployment. This means their Azure OpenAI Service prompts and responses will now be processed anywhere that the relevant model is deployed. By exchanging reservations from regional to global, they were able to apply their reservation savings ensuring seamless cost savings for their critical application. Term exchange: Contoso initially committed to a one-month reservation. However, they soon realized their need for ongoing service and wanted to allocate resources more efficiently. By exchanging reservations, they switched to a one-year term, allowing them to budget more effectively. Payment exchange: Contoso started with an upfront payment model. However, for better cash flow management, they transitioned to a monthly payment plan through a payment exchange. Changing the scope of provisioned reservations As Contoso’s use of Azure OpenAI Service expanded across multiple departments, they needed to modify their reservation scope. Azure offers the ability to scope reservations to individual resource groups or subscriptions, to subscriptions within a management group, or to all subscriptions within a billing account or billing profile. Contoso used Microsoft Cost Management to modify the scope of their reservations, ensuring that each department had the necessary resources. Setting up automatic renewals for provisioned reservations To prevent service disruptions and maintain budget predictability, Contoso enabled automatic renewal for their reservations. Automatic renewals offer several benefits: Continuous service: Ensures uninterrupted billing for Azure OpenAI Service. Budget predictability: Maintains consistent costs over time. Reduced administrative overhead: Eliminates the need for manual renewal processes. Enabling auto-renewal in the Azure Portal is a straightforward process, ensuring that Contoso’s AI operations continue uninterrupted. Reviewing the provisioned reservation utilization report Contoso’s finance and IT teams regularly review their provisioned reservation utilization report to ensure they are getting the best value from their investment. These reports, accessible through Azure Cost Management, provide insights into reservation usage and help identify areas for optimization. Analyzing utilization reports allows Contoso to: Identify underutilized resources. Adjust reservations to match actual usage. Optimize costs and improve efficiency. Setting up utilization alerts To proactively monitor their reservation usage, Contoso configured reservation utilization alerts in Microsoft Cost Management. These alerts notify them if usage drops below a set threshold, allowing them to take timely action. By setting up utilization alerts, Contoso can: Receive real-time notifications of usage changes. Adjust reservations to avoid waste. Maintain optimal resource utilization. Best practices for managing Azure OpenAI Service provisioned reservations Azure OpenAI Service provisioned reservations offer a powerful way to control costs, but proactive management is essential for maximizing their value. As we have seen, Contoso implemented several best practices to maximize the benefits of provisioned reservations: Regular usage monitoring: Continuously tracking usage to identify trends and optimize resource allocation. Strategic adjustments and exchanges: Adapting reservations to match evolving business needs. Implementing governance policies: Establishing clear policies for reservation management and usage. Automating alerts and reporting: Configuring alerts and reports to proactively monitor reservation usage. By leveraging the flexibility of reservation exchanges and implementing best practices, any business can optimize their AI investments and drive long-term efficiency. Embracing these strategies will empower your organization to fully capitalize on the transformative potential of Azure OpenAI Service. Find out more by completing the Azure OpenAI Service provisioned reservation learn module. Additional Resources: What are Azure reservations? Save costs with Microsoft Azure OpenAI Service provisioned reservations Azure OpenAI Service provisioned throughput units (PTU) onboarding Azure pricing overviewDiscover cost management opportunities using tailored Copilot in Azure prompts
In this blog we’re going to explore the value of incorporating Copilot in Azure in your Microsoft Cost Management tasks, and give some scenarios in which asking specific, fine-tuned prompts can yields the most helpful results.
