Announcing savings plan for databases: flexible savings for modern, evolving workloads
As organizations modernize their data platforms, database environments are constantly changing. Teams migrate between services, scale across regions, and evolve architectures to support new applications and AI driven workloads. But optimizing costs in these environments can be challenging, especially when savings options require locking into specific services, regions, or configurations. To help with these challenges, we’re introducing savings plan for databases, a new way to save on eligible database services while maintaining the flexibility needed to modernize and grow your business. Savings plan for databases helps IT, engineering, and FinOps teams reduce database costs with a simple, spend based commitment—without slowing down architectural change. What is savings plan for databases? Savings plan for databases is a spend based pricing model that enables customers to save on eligible database services by committing to a fixed hourly spend for one year. In return, customers receive lower prices—up to 35% compared to pay-as-you go pricing on select services*. Instead of committing to a specific database service, region, or configuration, customers commit to an hourly dollar amount (for example, $5 per hour). The plan automatically applies savings to eligible database usage each hour, prioritizing the usage that delivers the greatest savings first—across services and regions—until the hourly commitment is met. Any usage beyond the commitment continues at pay-as-you go rates, ensuring flexibility as needs change. Pricing is for illustrative purposes only. Benefits designed for modern database workloads Flexibility as environments evolve Savings plan for databases is designed for change. Savings continue to apply as workloads modernize or move across regions—without requiring customers to repurchase or reconfigure commitments. This makes it well suited for dynamic environments where architectures are expected to evolve over time. Automatic cost optimization With a single hourly commitment, customers unlock discounted prices on eligible database usage. Savings are applied automatically each hour, ensuring customers get the most value from their commitment without manual management or constant tuning. Predictable budgeting and forecasting A fixed hourly spend replaces variable pay-as-you-go costs with a predictable commitment, making it easier for FinOps and IT finance teams to forecast spend, plan budgets, and manage cloud investments with confidence. What services that are covered with the savings plan for databases: Savings plan for databases pricing How to purchase a savings plan for databases Getting started is simple: Review personalized recommendations in the Azure portal based on recent database usage. Choose an hourly commitment and scope it into a subscription, resource group, management group, or entire account. Select a payment option—pay monthly or upfront at no additional cost—and optionally enable auto‑renewal. Once purchased, savings are applied automatically to eligible database usage every hour. Learn more here on how to purchase a savings plans here. Example: saving while modernizing Consider a team running a global application that uses Azure SQL Database and Azure Database for PostgreSQL today, with plans to expand into additional regions over the next year. Instead of purchasing individual reservations tied to specific services or regions, the team purchases a 1‑year savings plan for databases with a $5/hour commitment. As usage shifts across database services and regions, Azure automatically applies discounted prices to eligible usage up to the hourly commitment. The team continues modernizing without disruption—while benefiting from predictable savings. Summary Savings plan for databases provides a new way to optimize database costs —without locking into fixed architectures. With hourly commitments, automatic optimization, and predictable costs, it’s designed to support modernization today and continued growth tomorrow. Get started You can begin saving today: View your recommendation in Azure Advisor Purchase a plan in Azure portal Visit the Savings plan homepage Review the Microsoft Documentation Watch demo videos at the Azure Essentials Show *Customers may see savings estimated to be between 0% and 35%. The 35% savings estimate is based on one Azure SQL Database serverless running for 12 months at a pay-as-you-go rate vs. a reduced rate for a 1-year savings plan. Based on Azure pricing as of March 2026. Prices are subject to change. Actual savings may vary based on location, database service, and/or usage. ** Note that savings plan for databases will also be consumed by SQL Server on Azure Virtual Machines and SQL Server enabled by Azure Arc hourly license at the normal pay-go price.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.Microsoft Agent Pre-Purchase Plan: One Unified Path to Scale AI Agents
AI is now essential, and at Microsoft Ignite 2025, we introduced a new foundation for intelligent agents: Work IQ, Fabric IQ, and Foundry IQ. These three IQs represent the intelligence layer that gives agents deep context; understanding how people work, connecting to enterprise data, and orchestrating knowledge across platforms. Together with the launch of Microsoft Agent Factory, organizations now have a unified program to build, deploy, and manage agents powered by these IQs. Microsoft Agent Pre-Purchase Plan (P3) is designed to for organizations looking to confidently invest in AI agent development with a single, predictable budget. It empowers businesses to experiment, build, and scale sophisticated AI agents without the friction of fragmented licensing or unexpected costs. By unifying access to agentic services across Microsoft Foundry, Microsoft Copilot Studio*, Microsoft Fabric, and GitHub, Microsoft Agent P3 empowers organizations to harness the full potential of the IQ layer thus removing barriers and unlocking the value of truly intelligent, context-driven agents. What is Microsoft Agent Pre-Purchase Plan and how to does it work? Microsoft Agent P3 is a one-year pay-upfront option. Customers commit upfront to a lump-sum pool of Agent Commit Units (ACU) that can be used at any time during the one-year term. Every time you consume eligible services across Microsoft Foundry, Microsoft Copilot Studio*, Microsoft Fabric, and GitHub, the ACUs 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. Pricing* *Pricing as of November 2025, subject to change. **Example if Microsoft Copilot Studio generates a retail cost of $100 based on Copilot Credit and Microsoft Foundry usage, then 100 Agent CUs (ACUs) are consumed. What is covered by the Microsoft Agent Pre-Purchase Plan? * List as of February 2026, subject to change ** Currently in Private Preview *** Covers Copilot Credits enabled agentic services: Microsoft Copilot Studio, Dynamics 365 first-party agents, and Copilot. Microsoft reserves the right to update Copilot Credit eligible products. Customer Example Suppose a customer expects to consume 1,500,000 Copilot Credit with custom agents created in Microsoft Copilot Studio. Assuming the pay-as-you-go rate for Copilot Credit to be $0.01, then at the pay-as-you-go rate, this will cost $15,000. In addition, if they are using 5000 Microsoft Foundry Provisioned Throughput Units (PTU) and assuming the pay-as-you-go rate for PTU to be $1, then at the pay-as-you-go rate, this will cost $5,000. By purchasing Tier 1 (20,000 ACUs) Microsoft Agent P3 at the cost of $19,000, it will give a 5% saving compared to the pay-as-you-go rate for the same usage. How to purchase a Microsoft Agent Pre-Purchase Plan? Sign in to the Azure portal →Reservations → + Add → Microsoft Agent Pre-Purchase Plan. Select your subscription and scope Choose your tier and complete payment. What sets Microsoft Agent Pre-Purchase Plan apart? At the heart of Microsoft Agent Pre-Purchase Plan are four pillars that redefine how organizations consume AI services: One Plan: A single offer that spans Microsoft Foundry, Microsoft Copilot Studio*, Microsoft Fabric, and GitHub. No more siloed credits or SKU-level complexity, just one pool for all your AI workloads. Breadth of Services: Access to 30+ services, from Azure AI Search and Cognitive Services to orchestration tools and Copilot-enabled experiences. One Governance Path: Simplifies procurement and budget management. Procurement teams gain visibility and control without sacrificing agility. Predictable Savings: Get discounts and avoid surprises when you choose this plan. Conclusion The Microsoft Agent Pre-Purchase Plan is designed to make your AI journey simpler, smarter, and more cost-effective. By combining the strengths of Microsoft Foundry, Microsoft Copilot Studio*, Microsoft Fabric, and GitHub into a single, unified offer, the plan eliminates the need to choose one platform or manage multiple contracts. Organizations benefit from predictable budgeting, streamlined procurement, and the flexibility to innovate across more than 30+ agentic services—all with one pool of funds. Whether you’re just starting with AI or scaling enterprise-wide adoption, the Microsoft Agent Pre-Purchase Plan empowers you to unlock the full value of Microsoft’s agentic platform—driving innovation, efficiency, and business impact. And with support for agents built on Work IQ, Fabric IQ, and Foundry IQ, customers can be confident their solutions are grounded in the latest intelligence announced at Ignite. What’s next Read the Microsoft Agent P3 Offer MS Learn Doc Purchase Microsoft Agent P3 in your Azure Portal * Covers Copilot Credits enabled agentic services: Microsoft Copilot Studio, Dynamics 365 first-party agents, and Copilot. Microsoft reserves the right to update Copilot Credit eligible productsManaging 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.What’s new in FinOps toolkit 12 – July 2025
This month, you’ll find support for FOCUS 1.2, autostart in FinOps hubs which can reduce your hub costs, a new page in the Cost summary Power BI report, and various small fixes, improvements, and documentation updates across the board. Read on for details.Moving to a Microsoft Customer Agreement (MCA) from an Enterprise Agreement (EA)
Microsoft has introduced a new contractual model for Azure enterprise customers, called the Microsoft Customer Agreement. Guidance on changes to the billing hierarchy can be found here: Set up billing for Microsoft Customer Agreement - Azure Microsoft Senior Cloud Solution Architect Dina Fatkulbayanova has written a great article on LinkedIn sharing her knowledge and recommendations of the transition process and billing hierarchy changes: Moving from EA to MCA: what you should know Check it out and please share with your networks on LinkedIn!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.
Azure VMWare (AVS) Cost Optimization Using Azure Migrate Tool
What is AVS? Azure VMware Solution provides private clouds that contain VMware vSphere clusters built from dedicated bare-metal Azure infrastructure. Azure VMware Solution is available in Azure Commercial and Azure Government. The minimum initial deployment is three hosts, with the option to add more hosts, up to a maximum of 16 hosts per cluster. All provisioned private clouds have VMware vCenter Server, VMware vSAN, VMware vSphere, and VMware NSX. As a result, you can migrate workloads from your on-premises environments, deploy new virtual machines (VMs), and consume Azure services from your private clouds. Learn More: https://learn.microsoft.com/en-us/azure/azure-vmware/introduction What is Azure Migrate Tool? Azure Migrate is a comprehensive service designed to help you plan and execute your migration to Azure. It provides a unified platform to discover, assess, and migrate your on-premises resources, including servers, databases, web apps, and virtual desktops, to Azure. The tool offers features like dependency analysis, cost estimation, and readiness assessments to ensure a smooth and efficient migration process. Learn More: https://learn.microsoft.com/en-us/azure/migrate/migrate-services-overview How Azure Migrate can be used to Discover and Assess AVS? Azure Migrate enables the discovery and assessment of Azure VMware Solution (AVS) environments by collecting inventory and performance data from on-premises VMware environments, either through direct integration with vCenter (via Appliance) or by importing data from tools like RVTools. Using Azure Migrate, organizations can analyze the compatibility of their VMware workloads for migration to AVS, assess costs, and evaluate performance requirements. The process involves creating an Azure Migrate project, discovering VMware VMs, and generating assessments that provide insights into resource utilization, right-sizing recommendations, and estimated costs in AVS. This streamlined approach helps plan and execute migrations effectively while ensuring workloads are optimized for the target AVS environment. Note: We will be narrating the RVtools Import method in this article. What Is RVTools? RVTools is a lightweight, free utility designed for VMware administrators to collect, analyze, and export detailed inventory and performance data from VMware vSphere environments. Developed by Rob de Veij, RVTools connects to vCenter or ESXi hosts using VMware's vSphere Management SDK to retrieve comprehensive information about the virtual infrastructure. Key Features of RVTools: Inventory Management: Provides detailed information about virtual machines (VMs), hosts, clusters, datastores, networks, and snapshots. Includes details like VM names, operating systems, IP addresses, resource allocations (CPU, memory, storage), and more. Performance Insights: Offers visibility into resource utilization, including CPU and memory usage, disk space, and VM states (e.g., powered on/off). Snapshot Analysis: Identifies unused or orphaned snapshots, helping to optimize storage and reduce overhead. Export to Excel: Allows users to export all collected data into an Excel spreadsheet (.xlsx) for analysis, reporting, and integration with tools like Azure Migrate. Health Checks: Identifies configuration issues, such as disconnected hosts, orphaned VMs, or outdated VMware Tools versions. User-Friendly Interface: Displays information in tabular form across multiple tabs, making it easy to navigate and analyze specific components of the VMware environment. Hand-on LAB Disclaimer: The data used for this LAB has no relationship with real world scenarios. This sample data is self-created by the author and purely for understanding the concept. To discover and assess your Azure VMware Solution (AVS) environment using an RVTools extract report in the Azure Migrate tool, follow these steps: Prerequisites RVTools Setup: Download and install RVTools from the RVTools Download Ensure connectivity to your vCenter server. Extract the data by running RVTools and saving the output as an Excel (.xlsx) file Permissions: You need at least the Contributor role on the Azure Migrate project. Ensure that you have appropriate permissions in your vCenter environment to collect inventory and performance data. File Requirements: The RVTools file must be saved in .xlsx format without renaming or modifying the tabs or column headers. Note: Sample Sheet: Please check the attachment included with this article. Note that this is not the complete format; some tabs and columns have been removed for simplicity. During the actual discovery and assessment process, please do not modify the tabs or columns. Procedure Step 1: Export Data from RVTools Follow the steps provided in official website to get RVTools Extract Sample Sheet: Please check the attachment included with this article. Note that this is not the complete format; some tabs and columns have been removed for simplicity. During the actual discovery and assessment process, please do not modify the tabs or columns. Step 2: Discover Log in to the Azure portal. Navigate to Azure Migrate and select your project or create new project. Under Migration goals, select Servers, databases and web apps. On Azure Migrate | Servers, databases and web apps page, under Assessment tools, select Discover and then select Using import. In Discover page, in File type, select VMware inventory (RVTools XLSX). In the Step 1: Import the file section, select the RVTools XLSX file and then select Import. Wait for some time to Import Once import completed check for Error Messages if any and rectify those and re upload, otherwise wait 10-15 minutes to reflect imported VMs in the discovery. Post discovery Reference Link: https://learn.microsoft.com/en-us/azure/migrate/vmware/tutorial-import-vmware-using-rvtools-xlsx?context=%2Fazure%2Fmigrate%2Fcontext%2Fvmware-context Step 3: Assess After the upload is complete, navigate to the Servers tab. Click on Assess -->Azure VMware Solution to assess the discovered machines. Edit assessment settings based on your requirements and Save Target region: Select the Azure region for the migration. Node Type: Specify the Azure VMware Solution series (e.g., AV36, AV36P). Pricing model: Select pay-as-you-go or reserved instance pricing. Discount: Specify any available discounts. Note: We will be explaining all the parameters in optimize session. As of now just review and leave parameters as it is. In Assess Servers, select Next. In Select servers to assess > Assessment name > specify a name for the assessment. In Select or create a group > select Create New and specify a group name. Select the appliance and select the servers you want to add to the group. Then select Next. In Review + create assessment, review the assessment details, and select Create Assessment to create the group and run the assessment. Step 4: Review the Assessment View an assessment In Windows, Linux and SQL Server > Azure Migrate: Discovery and assessment, select the number next to Azure VMware Solution. In Assessments, select an assessment to open it. As an example (estimations and costs, for example, only): Review the assessment summary. You can select Sizing assumptions to understand the assumptions that went in node sizing and resource utilization calculations. You can also edit the assessment properties or recalculate the assessment. Step 5: Optimize We have received a report without any optimization in our previous steps. Now we can follow below steps to optimize the cost and node count even further High level steps: Find limiting factor Find which component in settings are mapped for optimization depending on limiting factor Try to adjust the mapped component according to Scenario and Comfort Find Limiting factor: First understand which component (CPU, memory and storage) is deciding your ESXI Node count. This will be highlighted in the report The limiting factor shown in assessments could be CPU or memory or storage resources based on the utilization on nodes. It is the resource, which is limiting or determining the number of hosts/nodes required to accommodate the resources. For example, in an assessment if it was found that after migrating 8 VMware VMs to Azure VMware Solution, 50% of CPU resources will be utilized, 14% of memory is utilized and 18% of storage will be utilized on the 3 Av36 nodes and thus CPU is the limiting factor. Find which option in the setting can be used to optimize: This is depending on the limiting factor. For eg: If Limiting factor is CPU, which means you have high CPU requirement and CPU oversubscription can be used to optimize ESXI Node. Likewise, if storage is the limiting factor editing FTT, RAID or introducing External storage like ANF will help you to reduce Node count. Even reducing one node count will create a huge impact in dollar value. Let's understand how over commitment or over subscription works with simple example. Let's suppose I have two VMs with below specification Name CPU Memory Storage VM1 9 vCPU 200 GB 500 GB VM2 4 vCPU 200 GB 500 GB Total 13 vCPU 400 GB 1000 GB We have EXSI Node which has below capacity: vCPU 10 Memory 500 GB storage 1024 GB Now without optimization I need two ESXI node to accommodate 13 vCPU of total requirement. But let's suppose VM1 and VM2 doesn't consume entire capacity all the time. The total capacity usage at a time will not go beyond 10. then I can accommodate both VM in same ESXI node, Hence I can reduce my node count and cost. Which means it is possible to share resources among both VMs. Without optimization With optimization Parameters effecting Sizing and Pricing CPU Oversubscription Specifies the ratio of number of virtual cores tied to one physical core in the Azure VMware Solution node. The default value in the calculations is 4 vCPU:1 physical core in Azure VMware Solution. API users can set this value as an integer. Note that vCPU Oversubscription > 4:1 may impact workloads depending on their CPU usage. Memory overcommit factor Specifies the ratio of memory overcommit on the cluster. A value of 1 represents 100% memory use, 0.5, for example is 50%, and 2 would be using 200% of available memory. You can only add values from 0.5 to 10 up to one decimal place. Deduplication and compression factor Specifies the anticipated deduplication and compression factor for your workloads. Actual value can be obtained from on-premises vSAN or storage configurations. These vary by workload. A value of 3 would mean 3x so for 300GB disk only 100GB storage would be used. A value of 1 would mean no deduplication or compression. You can only add values from 1 to 10 up to one decimal place. FTT : How many device failure can be tolerated for a VM RAID : RAID stands for Redundant Arrays of Independent Disks Explains how data should be stored for redundancy Mirroring : Data will be duplicated as it is to another disk E.g.: To protect a 100 GB VM object by using RAID-1 (Mirroring) with an FTT of 1, you consume 200 GB. Erasure Coding : Erasure coding divides data into chunks and calculates parity information (redundant data) across multiple storage devices. This allows data reconstruction even if some chunks are lost, similar to RAID, but typically more space-efficient E.g.: to protect a 100 GB VM object by using RAID-5 (Erasure Coding) with an FTT of 1, you consume 133.33 GB. Comfort Factor: Azure Migrate considers a buffer (comfort factor) during assessment. This buffer is applied on top of server utilization data for VMs (CPU, memory and disk). The comfort factor accounts for issues such as seasonal usage, short performance history, and likely increases in future usage. For example, a 10-core VM with 20% utilization normally results in a 2-core VM. However, with a comfort factor of 2.0x, the result is a 4-core VM instead. AVS SKU Sizes Optimization Result In this example we got to know that CPU is my limiting factor hence I have adjusted CPU over subscription value from 4:1 to 8:1 Reduced node count from 6 (3 AV36P+3 AV64) to 5 AV36P Reduced Cost by 31% Note: Over-provisioning or over-committing can put your VMs at risk. However, in Azure Cloud, you can create alarms to warn you of unexpected demand increases and add new ESXi nodes on demand. This is the beauty of the cloud: if your resources are under-provisioned, you can scale up or down at any time. Running your resources in an optimized environment not only saves your budget but also allows you to allocate funds for more innovative ideas.
