Streamline Analytics Spend on Microsoft Fabric with Azure Reservations
Introduction As organizations accelerate their cloud adoption, optimizing spend while maintaining performance and scalability is a top priority. Microsoft Fabric, our all-in-one, software-as-a-service data platform, is designed to help teams accomplish any data project. But how can you ensure your investment delivers maximum value? In this post, we’ll explore how Azure reservations for Microsoft Fabric can help you optimize costs, simplify purchasing, and streamline your data analytics spend. What Is Microsoft Fabric? Microsoft Fabric is an end-to-end data platform that brings together data orchestration, transformation, machine learning, real-time event processing, reporting, and databases in a single SaaS experience. It’s designed for data engineers, scientists, analysts, and business users, offering role-specific workloads and integrated AI experiences, all backed by a unified data lake—OneLake. Unlike traditional services that require managing different pricing and capacities for each workload, Fabric simplifies this with a single capacity model. You purchase Fabric Capacity Units (CUs), which power all workloads, and jobs consume CUs at different rates depending on your compute needs. How Azure reservations Work with Fabric Azure reservation is a pricing model that helps you save when you commit to a specific resource, region, and term - ideal for stable, predictable workloads. Reservations don’t affect capacity or runtime; they simply provide discount benefits. Azure analyzes your usage and recommends reservation options, and tools like Azure Advisor guide you toward the right purchase. You may be familiar with how reservations work with virtual machines, but Azure reservations can also apply to other services such as Microsoft Fabric. When purchasing Fabric, you can choose a pay-as-you-go model for flexible usage or save substantially with Azure reservations for consistent workloads. For example, if you deploy an F64 SKU in Fabric for ongoing reporting and analytics, buying an Azure reservation for this SKU ensures you pay the discounted rate for your consistent usage. How to Purchase a Reservation for Microsoft Fabric Let’s look at how you can purchase a reservation for Microsoft Fabric. Follow these steps below or watch this video: 1. Start in the Microsoft Marketplace or Azure Portal: Visit the Microsoft Marketplace and look up Microsoft Fabric. Click “Get it now” and it will take you to the Azure Portal. 2. Create Fabric Capacity: Select Configuration: In Azure Portal, on the Create Fabric Capacity page, select your subscription and resource group, name your capacity, and select your region. Select Size: Use the Fabric SKU estimator to determine the right capacity for your workloads, or start with a free trial and monitor usage with the capacity metrics app. You can always upgrade or create multiple capacities as needed. Organize with Tags: Use Azure tags to track costs and automate management across environments. 3. Buy Fabric Reservations: In the Azure portal, search for and select “Reservations.” On the Reservations page, click “Add” and select Microsoft Fabric. Choose your scope and subscription, select Fabric Capacity with upfront or monthly payments, and adjust the quantity as needed. Best Practices for Maximizing Savings To get the most value from your reservations, follow these best practices: Estimate Carefully: Avoid over-committing (which leads to wasted resources) or under-committing (which leads to higher costs). Understand your resource needs and usage and use Azure Advisor recommendations to make informed decisions. Enable Auto-Renew: Ensure your reservations automatically renew so you don’t lose the discount but adjust your reservation needs as workloads change. Monitor Usage: Use Azure Cost Management to continuously track reservation usage. Choose the Right Scope: Align reservation benefits with your organizational structure to maximize savings. Conclusion: Microsoft Fabric and Azure reservations empower organizations to streamline analytics spend, simplify purchasing, and unlock significant savings without sacrificing performance or scalability. Following best practices and leveraging the right tools ensures your cloud investments deliver maximum value. Get started today by visiting the Microsoft Marketplace and Azure Portal to purchase Fabric Capacity units and reservations or read Save costs with Microsoft Fabric Capacity reservations - Microsoft Cost Management | Microsoft Learn to learn more.Smarter Cloud, Smarter Spend: How Azure Powers Cost-Efficient Innovation
In today’s dynamic business environment, organizations are under pressure to innovate rapidly while managing costs effectively. The cloud, especially with AI at the forefront, is driving transformation across industries. But with rising cloud spend and economic uncertainty, cost efficiency has become a top priority. To help customers navigate this challenge, Microsoft commissioned Forrester Consulting to conduct a Total Economic Impact™ (TEI) study. The study reveals how organizations can unlock significant financial and operational benefits by leveraging Microsoft Azure’s tools and strategic pricing offers, especially when migrating to the cloud and adopting AI. Cloud Migration: Accelerating Modernization with Azure Migrating to the cloud is a foundational step for digital transformation. But many organizations initially struggled with cost overruns, lack of visibility, and inefficient resource usage. The TEI study shows how Azure’s native tools and strategic pricing offers helped overcome these challenges. Key Enablers of Cost-Efficient Migration: Azure Hybrid Benefit: Allows organizations to optimize savings in their migration journey by giving a discount on server licenses and subscriptions and granting hosting and outsourcing benefits Azure reservations: A commitment model that enables customers to save on their spend when they commit to a specific resource, in a region, and for a specific term. Azure savings plan for compute: A flexible pricing model that enables customers to save on their cost when they commit to a spend a fixed hourly amount for a specific term. Microsoft Cost Management & Azure Advisor: Provide real-time insights and optimization recommendations. Azure Pricing Calculator: Accurately estimate migration costs and forecast budgets. “Azure reservations and Azure Hybrid Benefit facilitate moving to the cloud. With these offers and better cost management, we are saving 30% to 35%. Managing our costs without these tools would be an unpredictable nightmare.” — Senior IT Director, Manufacturing Quantified Impact*: 25% reduction in cloud spending in Year 1 using Microsoft tools. $4.9M in direct savings over three years from tool-based optimization. $3.8M in additional savings from strategic pricing offers. AI Adoption: Driving Innovation with Cost-Efficient Azure Solutions Once migrated, organizations are increasingly turning to AI to drive innovation and competitive advantage. Azure doesn’t just support AI workloads, it makes them more cost-effective and scalable. Key Enablers of AI Adoption: Azure AI Foundry Provisioned Throughput reservations: Get cost savings compared to provisioned throughput (PTU) hourly pricing. Microsoft Cost Management & Azure Advisor: Help forecast and optimize AI-related cloud spend. Strategic Pricing Offers: Enable predictable budgeting and reinvestment into AI initiatives. “Leveraging Microsoft tools and strategic pricing offers is not only increasing profitability but also putting those savings into more interesting projects like AI with fraud prevention work and the customer experience.” — VP of Analytics Engineering, Financial Services “Azure reservations for AI projects using PTUs has helped our AI initiatives. It has helped better predict costs.” — CIO, Healthcare Unquantified Benefits: Increased cloud insights and visibility. Improved governance and accountability. Enhanced productivity (24% by Year 3). The Bottom Line: Unified Transformation with Azure The TEI study concludes that Azure delivers a unified approach to cloud computing, enabling organizations to: Migrate to the cloud efficiently with cost control Adopt AI rapidly by reinvesting savings into innovation Achieve a net present value (NPV) of $8.7 million over three years Realize a 35% reduction in cloud spending in Year 1 “With Microsoft tools, we can focus on higher value projects. Instead of reviewing reports and conducting cost audits, we can build new tools and build with genAI. We can innovate faster.” — Vice President of Analytics Engineering, Financial Services Ready to Dive Deeper? This blog is just the beginning. The full Forrester TEI study is packed with insights, customer stories, and financial modeling to help you build your business case for Azure. Read the full study here *To understand benefits, costs, and risks, Forrester interviewed eight decision-makers with experience using the evaluated Azure solutions. For the purposes of this study, Forrester aggregated the results from these decision-makers into a single composite organization.Unlock Savings with Copilot Credit Pre-Purchase Plan
Introduction As organizations scale their use of Microsoft Copilot Studio from building custom agents to integrating with Dynamics 365, cost predictability and optimization becomes critical. To help you plan confidently and save more, we’re introducing Copilot Credit Pre-Purchase Plan (P3)*, a simple, one‑year plan that delivers volume discounts and automatic billing, so your team can focus on outcomes, not invoices. What is Copilot Credit Pre-Purchase Plan? The P3 is a one‑year, pay‑up‑front option for Copilot Credits. You purchase Copilot Credit Commit Units (CCCU) and your usage automatically draws from this pool. Higher tiers unlock progressive discounts enabling more growth. See pricing here. How it works You pre-purchase a pool of Copilot Credits Commit Unit (CCCUs) for one year. Every time you use Copilot Credits for Microsoft Copilot Studio, Dynamics 365 first-party agents, or Copilot Chat*, the CCCUs are automatically drawn down from your P3 balance. If you use up your balance before the year ends, you can add another P3 plan or switch to pay-as-you-go. If you don’t use all your credits by the end of the year, the remaining balance expires. Example: A retail company runs 15 custom Copilot Studio agents to handle inventory checks, store operations, and customer service. They expect seasonal spike (holiday campaigns, back-to-school, and clearance events) but want predictable costs. Without P3: On the pay-as-you-go model their total cost can vary and spike as their usage goes up and down. During peak months, usage surges, and so does the bill make budgeting tough. With P3: Based on forecasting they expect to use 1,500,000 Copilot Credits over 12 months. Because they know how many Copilot Credits they plan on consuming, the retail company buys P3: Purchase P3 Tier 2: 15,000 CCCUs (this covers 1,500,000 Copilot Credits) P3 Upfront cost: $14,100 P3 discount: 6% vs PAYG Now, every time their agents run, CCCUs are automatically deducted from the P3 balance. P3 Tiers: Pricing as of October 2025, subject to change. See pricing here. Key Benefits Cost savings: Up to 20% discount at highest tier**. Budget predictability: One‑year term with upfront payment and no surprise bills. Flexibility: Add more P3 anytime; works alongside capacity packs and PAYG. No redeployment: Applies automatically to eligible usage. Defined Scope: Decide where the P3 applies based on your business needs. It can be applied at the resource group, subscription, management group, or shared level. How to Purchase Copilot Studio Pre-purchase plan Sign in to the Azure portal → Reservations → + Add → Copilot Credit Pre‑purchase Plan. Select your subscription and scope Choose your tier and complete payment. Link your subscription to Copilot Studio in Power Platform Admin Center. Best Practices Estimate accurately: Use historical usage or Copilot consumption estimator. Deploy first: Ensure environments are PAYG‑enabled before buying. Monitor utilization: Set alerts to avoid unexpected PAYG charges. Plan for renewal: Auto‑renew is on by default; adjust as needed. Conclusion If you’re scaling fast or managing multiple environments, P3 gives you predictable costs and meaningful savings without sacrificing flexibility. It’s ideal for customers with variable or growing usage who want to optimize spend and simplify billing. Copilot Credit Pre-Purchase Plan makes it easier to innovate without worrying about unpredictable costs. By committing upfront, you unlock discounts, streamline billing, and gain confidence in your budget. Ready to start? Visit the Azure portal to purchase your plan today or read the Copilot Credits Pre-purchase Plans documents to learn more. Resources: Read how to confidently scale AI agent deployments with Copilot Studio Learn more about Microsoft Copilot Studio See Microsoft Copilot Studio Licensing Guide *Copilot Credit covers Microsoft Copilot Studio, Dynamics 365 first-party agents, and Copilot Chat. Microsoft reserves the right to update Copilot Credit eligible products. ** The actual realized cost per Copilot Credit for P3 and the MCS license will depend on the utilization rate. As such customers should take into account their expected usage pattern as P3 Commit Units expire annually while the Copilot Credit capacity packs expire monthly.Provider-Managed Azure Subscriptions: Cost Control and Commitment Clarity
As a Microsoft Cloud Solution Architect supporting enterprise customers, I occasionally encounter a specific scenario where customers with an Enterprise Agreement (EA) or Microsoft Customer Agreement (MCA-E) allow a service provider (SP) to manage one or more of their Azure subscriptions via the SP’s tenant. This setup has notable implications for cost and commitment management, which I’ll explore in this article. Recommended prerequisite reading: Microsoft Cost Management: Billing & Trust Relationships Explained Scenario Overview A customer signs a contract with a service provider to outsource the management of certain resources. The customer retains full control over resource pricing and expects the usage of these resources to contribute towards their Microsoft Azure Consumption Commitment (MACC). To achieve this, the customer associates one or more Azure subscriptions with a Microsoft Entra ID tenant owned and managed by the SP. In our example, this is “Subscription B.” The SP gains full RBAC access to the subscription and its resources, while the billing relationship remains tied to the customer’s billing account (EA) or billing profile (MCA-E). Let’s have a look at the implications from both the customers and the service providers perspective: Customers perspective Cost & Pricing All cost in Subscription B that occurs because of resource usage are tied and therefore billed to the customers billing account (EA) or billing profile (MCA-E). The prices used for the usage are based on the negotiated customer price list associated with the billing account (EA) /profile (MCA-E). The Azure resource consumption of Subscription B plus any eligible Marketplace offer consumption within the subscription contributes to the MACC of the customer. Customer has full cost visibility of Subscription B via Azure Cost Analysis on the billing account/billing profile level. Commitments (Reservations / Savings Plans) Shared commitments at the billing account/billing profile level are utilized by matching resources in Subscription B. Commitments scoped to Subscription B or lower can only be purchased by the customer, if the customer has RBAC rights on the subscription and the global billing policy allows purchases for subscription owner / reservation purchasers. Service Provider Perspective Cost & Pricing The service provider is responsible for managing Subscription B’s resources and the associated costs. Subscription B’s actual and amortized cost view is limited for the service provider as they have only access at the subscription level. The service provider has no direct access to the customer price (Price Sheet) or invoice information. Commitments (Reservations / Savings Plans) The service provider can purchase commitments scoped at Subscription B or lower (resource group) if the global customer’s billing policy allows purchases for subscription owners / reservation purchasers. The associated costs of the commitment are attributed to the customer’s billing account/profile. Shared or management group scoped commitments purchased by the service provider based on their own billing account / billing profile do not apply to Subscription B. Key take aways Decoupled Ownership: Customers can separate subscription management from billing ownership, enabling flexible operational models. Cost Control: Customers retain full visibility and control over pricing, cost allocation, and commitment utilisation—even when subscriptions are managed by a service provider. Governance and Policy Alignment: Successful implementation depends on clear billing policies and RBAC configurations that align with both customer and provider responsibilities.Understanding the Total Cost of Ownership
Whether you're just beginning your journey in Azure or are already managing workloads in the cloud, it's essential to ground your strategy in proven guidance. The Microsoft Cloud Adoption Framework for Azure offers a comprehensive set of best practices, documentation, and tools to help you align your cloud adoption efforts with business goals. One of the foundational steps in this journey is understanding the financial implications of cloud migration. When evaluating the migration of workloads to Azure, calculating the Total Cost of Ownership (TCO) is a crucial step. TCO is a comprehensive metric that includes all cost components over the life of the resource. A well-constructed TCO analysis can provide valuable insights that aid in decision-making and drive financial efficiencies. By understanding the comprehensive costs associated with moving to Azure, you can make informed choices that align with your business goals and budget. Here is a breakdown of the main elements that you need to build your own TCO: 1. Current infrastructure configuration: Servers: details about your existing servers, including the number of servers, their specifications (CPU, memory, storage), and operating systems. Databases: information about your current databases, such as the type, size, and any associated licensing costs. Storage: type and amount of storage you are currently using, including any redundancy or backup solutions. Network Traffic: Account for outbound network traffic and any associated costs. 2. Azure Environment Configuration: Virtual Machines (VMs): appropriate Azure VMs that match your current server specifications. This has to be based on CPU, memory, storage, and region. Storage Options: type of storage (e.g., Standard HDD, Premium SSD), access tiers, and redundancy options that align with your needs. Networking: networking components, including virtual networks, load balancers, and bandwidth requirements. 3. Operational Costs: Power and Cooling: Estimate the costs associated with power and cooling for your on-premises infrastructure. IT Labor: Include the costs of IT labor required to manage and maintain your current infrastructure. Software Licensing: Account for any software licensing costs that will be incurred in both the current and Azure environments. Once you have more clarity of these inputs you can complement your analysis with other tools depending on your needs. The Azure Pricing Calculator is well suited to providing granular cost estimation for different Azure services and products. However, if the intent is to estimate cost and savings during migrations, Azure Migrate business case feature should be the preferred approach as it will allow the user to perform detailed financial analysis (TCO/ROI) for the best path forward and assess readiness to move workloads to Azure with confidence. Understand your Azure costs The Azure pricing calculator is a free cost management tool that allows users to understand and estimate costs of Azure Services and products. It serves as the only unauthenticated experience that allows you to configure and budget the expected cost of deploying solutions in Azure The Azure pricing calculator is key for properly adopting Azure. Whether you are in a discovery phase and trying to figure out what to use, what offers to apply or in a post purchase phase where you are trying to optimize your environment and see your negotiated prices, the azure pricing calculator fulfills both new users and existing customers' needs. The Azure pricing calculator allows organizations to plan and forecast cloud expenses, evaluate different configurations and pricing models, and make informed decisions about service selection and deployment options. Decide, plan, and execute your migration to Azure Azure Migrateis Microsoft’s free platform for migrating to and modernizing in Azure. It provides capabilities for discovery, business case (TCO/ROI), assessments, planning and migration in a workload agnostic manner. Customers must have an Azure account and create a migration project within the Azure portal to get started. Azure Migrate supports various migration scenarios, including for VMware and Hyper-V virtual machines (VM), physical servers, databases, and web apps. The service offers accurate appliance based and manual discovery options, to cater to customer needs. The Azure Migrate process consists of three main phases: Decide, Plan, and Execute. In the Decide phase, organizations discover their IT estate through several supported methods and can get a dependency map for their applications to help collocate all resources belonging to an application. Using the data discovered, one can also estimate costs and savings through the business case (TCO/ROI) feature. In the Plan phase, customers can assess for readiness to migrate, get right-sized recommendations for targets in Azure and tools to use for their migration strategy (IaaS/PaaS). Users can also create a migration plan consisting of iterative “waves” where each wave has all dependent workloads for applications to be moved during a maintenance window. Finally, the Execute phase focuses on the actual migration of workloads to a test environment in Azure in a phased manner to ensure a non-disruptive and efficient transition to Azure. A crucial step in the Azure Migrate process is building a business case prior to the move, which helps organizations understand the value Azure can bring to their business. The business case capability highlights the total cost of ownership (TCO) with discounts and compares cost and savings between on-premises and Azure including end-of-support (EOS) Windows OS and SQL versions. It provides year-on-year cash flow analysis with resource utilization insights and identifies quick wins for migration and modernization with an emphasis on long-term cost savings by transitioning from a capital expenditure model to an operating expenditure model, paying only for what is used. Understanding the Total Cost of Ownership (TCO) is essential for making informed decisions when migrating workloads to Azure. By thoroughly evaluating all cost components, including infrastructure, operational, facilities, licensing and migration costs, organizations can optimize their cloud strategy and achieve financial efficiencies. Utilize tools like the Azure Pricing Calculator and Azure Migrate to gain comprehensive insights and ensure a smooth transition to the cloud.
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.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.What’s new in FinOps toolkit 0.5 – August 2024
In August, the FinOps toolkit 0.5 added support for Power BI reports on top of Cost Management exports without needing to deploy FinOps hubs; expanded optimization options in workbooks; improved optimization, security, and resiliency in Azure Optimization Engine; a new FOCUS article to help compare with actual/amortized data; and many smaller fixes and improvements across the board.
