Locations

Resources

Careers

Contact

Contact us

Azure Optimization

Optimizing Azure Cloud Spend and Commitments

Optimizing Azure Cloud Spend and Commitments

Optimizing Azure Cloud Spend and Commitments

Enterprise IT leaders face growing pressure to align Azure cloud investments with business outcomes while maintaining financial discipline.

An effective cost-optimization strategy is holistic, covering current usage analysis, licensing structures, capacity commitments, and organizational practices. Below are strategic areas to address, with practical steps, considerations, and impact examples.

Assessing Current Azure Spend

Establish a clear, detailed picture of the organization’s existing Azure expenditure. This involves:

  • Inventory and Categorize: Use Azure Cost Management (or a third-party tool) to break down costs by subscription, resource group, service (compute, storage, networking), and tag. Tag resources by department, project, or environment (Dev/Test vs Production) to attribute costs accurately.
  • Analyze Spending Trends: Review 6–12 months of usage data to spot spikes, seasonal patterns, or anomalies (e.g., unexpected surges or idle resources running 24×7). Identifying underused services (unused VMs, unattached disks, etc.) or inefficient configurations helps prioritize optimizations.
  • Benchmark and Compare: Compare costs against internal budgets or industry benchmarks. Confirm that spending aligns with business initiatives (e.,g. a new application rollout) and identify any unexplained growth.
  • Identify High-Impact Resources: Focus on the largest cost centres. If compute (VMs, databases) is 50–70% of spend, and many VMs run at <20% CPU utilization, those are prime candidates for savings. Similarly, large storage or network egress charges warrant review.
  • Example: A manufacturing firm tagged all resources by factory and environment. Analysis showed one dev/test region consumed 40% of the Azure bill despite minimal use. By consolidating test environments and shutting down unused VMs off-hours, they cut overall spending by ~30%.

Impact: This assessment establishes a baseline and highlights waste, giving finance and IT leaders visibility into where money is going. A clear cost picture drives priority actions and prevents budget surprises.

Understanding Microsoft’s Licensing and Pricing Structures

Azure’s cost is shaped by Microsoft’s licensing models, commitments, and data centre pricing. Key considerations include:

  • Subscription Agreements: Enterprises often use Enterprise Agreements (EA) or Microsoft Customer Agreements (Azure Plan) for Azure. These involve annual consumption commitments (MACC) that bring discounts and cloud credits. Smaller units may use Cloud Solution Provider (CSP) agreements or pay-as-you-go subscriptions. Each model has different billing and discount terms.
  • Pay-as-You-Go vs. Commit: Uncommitted (pay-as-you-go) rates are highest. Committing to usage—via Azure Reservations, Savings Plans, or EA MACC—yields deeper discounts. Understand which services in your agreement consume pre-paid credits versus those billed at on-demand rates.
  • Licensing Benefits: Leverage license-based savings like Azure Hybrid Benefit (AHB). For example, running Windows Server or SQL Server in Azure with existing on-premises licenses (with Software Assurance) eliminates separate OS/license charges, often saving ~30–40% on those workloads.
  • Regional and Tier Pricing: Azure service costs vary by region and tier. When permissible, consolidate workloads in cost-effective regions and avoid premium tiers unless needed. For instance, migrating non-critical dev/test workloads to a lower-cost region or a standard-performance tier can significantly reduce compute/storage bills.
  • Example: A global enterprise used its EA’s Azure spend credits with a three-year Reserved Instance commitment for steady computing. By applying Azure Hybrid Benefit on 70 Windows Server licenses, they eliminated OS fees on those VMs, cutting their compute bill by ~35%.

Impact: Deep knowledge of pricing and licensing avoids hidden costs. It ensures enterprise agreements and commitments are structured to fully utilize cloud credits and benefits, preventing overpayment.

This alignment can unlock substantial discounts and budget predictability.

Rightsizing Azure Commitments (RI, Savings Plans, MACC)

Commit to the right level of cloud resources by matching long-term purchases to real demand:

  • Analyze Stable Workloads: Identify workloads with predictable, continuous usage (e.g. production VMs or databases running 24×7). Use historical metrics to determine baseline capacity. Purchase Azure Reserved Instances (1- or 3-year) for these VMs or services; a 1-year RI can cut costs by ~40–60%, while 3-year RIs (especially combined with AHB) can save up to ~70%.
  • Apply Azure Savings Plans: For compute workloads that vary (development, testing, analytics), use 1- or 3-year Azure Savings Plans for Compute. These commit to a fixed hourly spend on any Azure compute (VMs, containers) in exchange for up to a ~65% discount. Unlike RIs, a Savings Plan’s discount automatically shifts across different VM sizes or regions, providing flexibility as workloads evolve.
  • Track MACC Commitments: Under an EA, manage the Azure Consumption Commitment (MACC) by forecasting and monitoring spending monthly. Aim to meet—but not significantly exceed—this commitment. Hitting it provides negotiated discounts, but under-spending means a wasted prepaid budget. Use Azure Cost Management to compare actual usage vs. MACC and set alerts at thresholds (e.g., 80% of commitment) to stay on track.
  • Review and Adjust Regularly: Workload patterns change. Every quarter, revisit your commitment utilization. Exchange or cancel underused RIs (Azure allows some flexibility) and consider new RIs/Savings if usage grows. Leverage Azure Advisor recommendations on reservations and savings for guidance.
  • Key Commitment Options (Summary): Commitment Type Term Applicable to Approx. Discount (vs PAYG) Flexibility Azure Reserved Instance (RI)1 or 3 years specific VM SKUs/service tiers Up to ~72% Low – tied to specific VM sizes/region. Azure Savings Plan (Compute)1 or 3 years Azure compute to ~65% High – automatically covers any VM/container EA Consumption Commitment (MACC)1–3 years. All Azure spend~10–20% (additional discount tiers). Medium – covers total Azure consumption.
  • Example: A retailer identified its front-end web and database VMs that ran constantly. They purchased 3-year RIs for those VMs (~60% off pay-as-you-go) and a 1-year Savings Plan for unpredictable background analytics. After going live, they tracked usage and realized one RI was no longer needed; they exchanged it for a smaller VM size, saving another 5% on compute costs.

Impact: Properly sized commitments turn predictable demand into budget savings. Enterprises often see double-digit percentage reductions in computing spend by reserving steady workloads and flexibly covering the rest. This ensures funds aren’t wasted on idle capacity and that large discounts are secured for steady usage.

Strategies for Enterprise Agreement (EA) Optimization

Optimizing an EA means aligning the contract to real needs and costs:

  • Audit EA Usage Pre-Renewal: Six to twelve months before renewal, reconcile deployed resources and software against EA entitlements. Identify surplus or underused licenses (e.g., extra SQL Server cores or Office seats) and eliminate or reallocate them. Document future needs so your next EA commitment matches reality.
  • Leverage Cloud Incentives: EAs often include prepaid Azure credits and rate discounts. Ensure those credits are fully utilized. If past credits went unused, renegotiate a lower Azure spend for the next term to avoid waste. If growth is planned (new apps, migrations), consider increasing the Azure commitment to obtain deeper discount tiers on rates.
  • Mix Agreements Strategically: For smaller business units or temporary projects, consider alternatives to the EA (such as CSP or pay-as-you-go subscriptions). This keeps low-volume workloads out of the main EA minimums. For example, a small acquired company can be put on CSP or separate contracts if it only needs a few cloud resources.
  • Use Software Assurance Benefits: Software Assurance (or its successor programs) provides cloud benefits in your EA. Ensure you apply Azure Hybrid Benefit, license mobility, and other perks. For instance, maintain active SA on an on-prem SQL Server if you plan to run those databases in Azure.
  • Segregate Non-Core Workloads: Isolate experimental, dev/test, or highly variable workloads into separate subscriptions or agreements. This prevents unpredictable costs from polluting the core EA budget and makes forecasting easier.
  • Example: Before renewing a global EA, a tech company discovered hundreds of unused SQL Server licenses. They reduced the renewal count and shifted those budget dollars into an increased Azure commitment. This meant future databases ran on flexible cloud capacity rather than expensive on-prem licenses, saving them millions over the contract term.

Impact: A well-optimized EA lowers licensing costs and aligns spending with actual needs. It helps avoid overbuying licenses or missing cloud discounts. Ultimately, it reduces budget overruns and strengthens your negotiating position since commitments and needs are transparent and justifiable.

Using Forecasting to Align Commitments to Real Demand

Proactive forecasting keeps commitments in step with business needs:

  • Collaborate Cross-Functionally: Tie Azure cost forecasts to upcoming business initiatives, seasonal cycles, and growth plans. Involve finance, application owners, and IT leadership. For example, if a new online service is launching, include its projected compute/storage needs in the forecast.
  • Use Historical Data and Tools: Leverage Azure Cost Management or BI tools to analyze past spending and project trends. Incorporate known changes (e.g., legacy system retirements or new hardware purchases), so forecasts aren’t just straight-line. Consider multiple scenarios: baseline demand, a planned growth scenario, and a conservative scenario to stress-test commitments.
  • Iterate on Forecasts: Forecasting is not one-and-done. Review actual usage against the forecast every quarter. If demand is lower, consider reducing future commitments or opting for more on-demand usage. If demand spikes beyond forecasts, adjust by adding reservations or scaling services promptly.
  • Avoid “Use It or Lose It” Pitfalls: Overestimating needs can lead to unspent commitment; underestimating means missing discounts or incurring penalties on shortfall. A common practice is to commit to ~90–95% of the forecasted core usage and keep a buffer on demand. Set alerts to warn if spending diverges from the plan mid-term.
  • Example: An online gaming company used analytics on the past six months of Azure usage to forecast server demand during peak gaming seasons. They then aligned their reservation purchases just before each quarter. When an actual launch exceeded expectations by 10%, they quickly bought additional short-term Savings Plans. This approach met peak demand without significant idle spending.

Impact: Accurate forecasts reduce financial risk. They prevent paying full price for unexpected overages or wasting money on unused commitments. Over time, forecast-driven commitments make Azure costs more predictable and ensure resources are available for strategic projects rather than idle surplus.

Managing Overprovisioning and Unused Resources

Tackling resource sprawl and idle assets yields immediate savings:

  • Automated Discovery and Reporting: Use Azure Advisor, Cost Management, or third-party tools to identify idle or underutilized resources (e.g., VMs with consistently low CPU/RAM usage, unattached disks, obsolete snapshots). Schedule regular reports so these findings surface automatically.
  • Enforce Rightsizing: Downsize oversized VMs or databases based on utilization data (e.g., move a lightly used 8-core VM to 4 cores). Adjust performance tiers for storage and services (for instance, switch from premium to standard tier if IOPS allow).
  • Auto-Shutdown and Scaling: Implement automation to shut down or scale non-production VMs outside business hours. Use Azure Automation, Dev/Test Labs, or scheduled Runbooks to turn off idle VMs overnight or on weekends and automatically start them when needed.
  • Clean Up Orphaned Assets: Remove stray resources (orphaned disks, IP addresses, snapshots). Establish a periodic cleanup process (or use tools like Azure Resource Cleaner scripts) to delete these unnecessary cost items.
  • Policy Enforcement: Apply Azure Policy to prevent waste: for example, restrict the allowed VM SKUs in each subscription so teams can’t provision excessively large instances. Enforce mandatory tags and resource naming standards so all assets are identifiable for cost tracking.
  • Example: A financial services firm found its dev team had deployed dozens of high-end VMs for simple tasks. By applying a policy limiting dev/test VM sizes and scheduling all dev VMs to auto-shutdown at 7 pm, they cut dev/test compute costs by ~40% almost overnight.

Impact: Regular cleanup and rightsizing eliminate clear waste, often freeing up 10–20% of the Azure budget. It also instills a cost-aware mindset in teams. Continuous enforcement blocks unnecessary spending at the source, ensuring only needed capacity is paid for.

Leveraging Independent Licensing Expertise

Independent advisors bring deep, unbiased knowledge to optimize cloud agreements:

  • Specialized Knowledge: Firms like Redress Compliance focus on licensing and cloud cost strategy. They know the intricacies of EA terms, Azure plans, and MACC commitments and can spot savings that in-house teams might miss.
  • Audit and Reconciliation: An independent review compares Azure usage and software deployment against licenses and commitments. Advisors can uncover unused licenses or suggest reallocating licenses to the cloud. For example, they may identify that moving workloads to Azure allows the reuse of on-prem licenses, avoiding new purchases.
  • Negotiation Guidance: As renewals approach, experts can model scenarios and suggest contract structures (e.g. optimal split between on-prem licenses vs cloud credits). They advise on how to phrase requirements and where to push for incentives (cloud credits, enhanced rates) based on market leverage.
  • Vendor-Neutral Perspective: Unlike vendor reps, independent consultants benchmark your situation against many customers. They highlight best practices and show how peers allocate spend. This helps CIOs confidently adjust their strategy without vendor bias.
  • Example: A multinational corporation engaged a licensing consultancy to audit its Azure usage. The consultants discovered redundant software assurance in the EA and recommended shifting that spending to Azure MACC. This reallocation cut their projected cloud costs by 15% while keeping capabilities constant.

Impact: Expert advisors accelerate optimization. They often find hidden savings and ensure licensing compliance, reducing audit risk. Their guidance can translate into double-digit cost reductions and smoother contract negotiations, giving CIOs confidence in their spending strategy.

Cost Governance and Cross-Team Accountability

Strong governance processes ensure cost discipline across the organization:

  • Cloud FinOps Culture: Establish a cross-functional FinOps team (finance, IT, development) that defines budgets, policies, and regular reviews. This team holds monthly or quarterly cost reviews, during which teams report on their spending and justify deviations. Embedding cost responsibility across IT and business units turns optimization into routine practice.
  • Chargeback/Showback Models: Tag resources by business unit or project so you can allocate costs. When each team sees its own Azure bill, it creates accountability. Some organizations make cloud cost efficiency a KPI, e.g., rewarding teams that stay under budget. Chargeback (billing teams for their usage) or showback (reporting usage) both increase visibility.
  • Budgeting and Alerts: Set budgets in Azure Cost Management at the subscription or resource-group level. To notify stakeholders, define alert thresholds (e.g., 80%, 100% of the budget). These alerts (via email or service hooks) trigger reviews or automatic actions (like pausing a non-critical workload when a budget cap is hit).
  • Enforce Governance Policies: Define an approved architecture and service catalogue. Use Azure Policy to enforce it (e.g., allow only certain VM sizes or tagged resources). This prevents uncontrolled consumption. Project approvals are required for significant new cloud spending.
  • Executive Oversight: Include cloud cost metrics in IT steering committees. Transparency with leadership (CFO, CIO, business heads) ensures financial targets are taken seriously. For example, present savings realized or cost avoidance achieved each quarter.
  • Example: A biotech firm implemented chargeback dashboards showing cthe ost per research team. One team noticed a big bill for a legacy data platform and replaced it with a scalable serverless solution, cutting that expense by 25%. Management publicly recognized the team’s cost savings, reinforcing accountability.

Impact: Governance structures and accountability make cost optimization sustainable. They typically eliminate repeated overspending and ensure every dollar spent supports a business goal. Over time, this discipline holds down costs and aligns IT spending with strategy rather than leaving cloud bills to fluctuate unchecked.

Tooling and Automation for Cost Visibility

The right tools amplify visibility and enforce policies at scale:

  • Native Azure Tools: Leverage Azure Cost Management + Billing for dashboards, cost allocation, and alerts. Use Azure Advisor to get personalized recommendations (e.g., identify unused reservations or downsize VMs). Apply Azure Policy for governance enforcement (e.g., tag compliance, allowed VM SKUs). These built-in tools provide no extra-cost visibility and control.
  • FinOps Platforms: Consider third-party cloud cost management platforms (e.g., CloudHealth, Apptio, CloudZero, Finout) that aggregate multi-cloud usage, provide advanced analytics, and forecast costs using AI/ML. These often include automated rightsizing suggestions and chargeback reporting. They complement native tools, especially for complex environments.
  • Tagging and Automation: Implement tagging rules automatically (via Azure Policy or Resource Manager templates) so new resources include department/project tags. Use infrastructure-as-code (ARM/Bicep, Terraform) with parameterized sizing to prevent ad-hoc provisioning of expensive VMs. Automate routine actions: for example, use Azure Automation or event-driven runbooks to shut down test VMs at night.
  • Integration and Marketplace: Encourage procurement via Azure Marketplace or approved third-party offerings that automatically count toward your Azure commitment (helping meet MACC). Integrate cost checks into CI/CD pipelines (e.g., use Azure’s Cost Management APIs to estimate changes pre-deployment). This “shift-left” approach catches high costs early.
  • Example: A media company built an internal Power BI dashboard by exporting Azure billing data nightly. Combined with automated tagging enforcement, this gave near real-time cost visibility. When a sudden spike occurred, automated alerts and a runbook paused a non-critical batch job, preventing an unexpected $50K charge.

Impact: Automation and visibility tools scale oversight across all teams. They turn raw data into actionable insights and enforce guardrails without manual effort. As a result, teams detect and address cost issues before they spiral, ensuring ongoing fiscal control.

Practical Summary of Actions and Strategic Impact

Organizations that proactively optimize Azure spending gain better budget control, higher efficiency, and strategic flexibility.

Key recommended actions and their impacts include:

  • Conduct a Comprehensive Cost Audit. Identify top spend drivers and waste. The impact isthe immediate identification of savings opportunities and risk areas.
  • Enforce Proper Tagging and Reporting: Classify resources by cost centre or project. Impact: Enables accountability and chargeback, preventing unchecked spending.
  • Apply Azure Licensing Benefits: Use Azure Hybrid Benefit, reservations, and savings plans for consistent workloads. Impact: Cuts per-unit costs by up to ~50% or more for eligible workloads.
  • Adjust Azure Commitments Based on Usage: Match RIs/Savings Plans/MACC to realistic forecasts. Impact: Avoids overpayment for unused capacity and secures optimal discount levels.
  • Regularly Reevaluate and Right-Size Resources: Set a cadence for downscaling VMs, deleting idle assets, and updating budgets. Impact: Prevents gradual budget creep and maintains efficiency as the environment evolves.
  • Implement Strong Governance: Establish a FinOps practice, budgeting process, and policies to monitor and control cloud spending. Impact: Sustains cost discipline and aligns technology use with business goals.
  • Seek Independent Advice: Engage independent licensing experts (e.g., Redress Compliance) for audits and strategy. Impact: Unbiased guidance uncovers hidden savings and mitigates compliance risk.
  • Leverage Automation: Automate alerting and enforcement (budgets, policies, shutdown schedules). Impact: Scales cost management across the enterprise with minimal manual oversight.

Strategic Impact: By following these actions, enterprises typically improve cost efficiency by double-digit percentages, freeing funds for innovation. More importantly, they gain confidence that the cloud strategy is sustainable: costs become predictable and tied to real demand rather than erratic line items. Ultimately, this discipline supports digital transformation goals with no wasted spending or compliance surprises.

Author

  • Fredrik Filipsson

    Fredrik Filipsson brings two decades of Oracle license management experience, including a nine-year tenure at Oracle and 11 years in Oracle license consulting. His expertise extends across leading IT corporations like IBM, enriching his profile with a broad spectrum of software and cloud projects. Filipsson's proficiency encompasses IBM, SAP, Microsoft, and Salesforce platforms, alongside significant involvement in Microsoft Copilot and AI initiatives, improving organizational efficiency.

    View all posts