AI infrastructure budgeting template: Plan your costs effectively

A survey of 224 senior IT leaders in the U.S. and Europe found that the share of overall IT spending devoted to gen-AI projects is on track to triple by 2025. This surge reflects AI’s transformative promise, but it also amplifies the financial challenges that come with operating GPU-intensive workloads at scale.

Surprisingly, more money hasn't necessarily translated to better budget control. In 2024, 83% of organizations exceeded their cloud budgets, primarily due to the overlooked cost of idle GPU reservations, unexpected network egress fees, and redundant storage costs.

These hidden inefficiencies continue to inflate spending because most teams still lack granular visibility into where, when, and why their AI budget leaks occur, making it impossible to implement meaningful solutions.

This article offers a practical, plug-and-play AI infrastructure budgeting template. By itemizing every cost driver, stress-testing multiple growth scenarios, and applying reliable forecasting methods, you'll have numbers everyone can trust to make informed, strategic decisions.

How to budget for AI workloads

Before plugging numbers into any budgeting template, adopt a FinOps-first mindset by treating every GPU hour, terabyte of storage, and gigabit of bandwidth as a measurable, predictable, and optimizable component of your spending portfolio.

Here are five things you need to know:

1. Think in lifecycle cost curves

AI workloads rarely burn cash at a consistent rate. Instead, they progress through three distinct phases:

Mapping your pipeline to these lifecycle phases enables you to forecast clearly when capital expenditure (CapEx) commitments offer advantages and when the flexibility of pay-as-you-go operating expenditure (OpEx) models is more suitable.

2. Choose your CapEx vs. OpEx blend strategically

Buying GPU servers outright locks in performance but ties up capital and risks rapid technological obsolescence, which is why people are increasingly preferring GPU infrastructure through OpEx models, such as leasing or hardware-as-a-service, to smooth cash flow and closely align costs with actual usage (liontechfinance.com).

Yet CapEx isn’t obsolete. Organizations adopting hybrid budgets typically reserve CapEx spending for:

• Stable, continuous inference workloads that benefit from depreciation.
• Long-term data-center investments (e.g., power and cooling upgrades) to maintain efficiency and control long-term operational metrics (PUE).

Use lifecycle cost curves to categorize expenses clearly into CapEx and OpEx buckets, enabling informed and strategic conversations with your CFO.

3. Tag everything, allocate precisely

According to the FinOps Foundation’s 2024 report, accurately allocating costs is the top priority for managing AI spending. To achieve this, establish a mandatory tagging policy (project, team, pipeline phase, and environment), which will ensure every entry in your billing exports aligns directly with a project or team need, enabling precise dashboards, accurate budgeting, and proactive alerting.

4. Forecast using guardrails

Budget forecasts for AI workloads should be grounded in realistic guardrails rather than guesswork. Apply these methods:

• Scenario modeling: Create “expected,” “stretch,” and “stress” scenarios, particularly for volatile phases like training peaks, to prepare for uncertainty
• Waste budgets: According to a survey, “reducing waste and managing commitments” ranks as teams’ top budgeting priority. Limit idle GPU spending to a predefined threshold, proactively incentivizing optimizations.
• Growth checkpoints: Cloud AI investment is accelerating, with hyperscalers projected to invest $315 billion this year, largely driven by AI initiatives. Conduct quarterly budget reviews to adapt quickly to market shifts, pricing changes, and hardware refresh cycles.

5. Don’t overlook non-GPU costs

GPUs represent only part of the total cost of ownership (TCO). Cooling systems, high-performance networking infrastructure, and operational personnel can account for over 50% of your AI infrastructure budget. Clearly outline these non-GPU line items to prevent surprise costs.

By adopting this comprehensive FinOps strategy—mapping lifecycle curves, strategically blending CapEx and OpEx spending, enforcing ironclad tagging policies, scenario-based forecasting, and maintaining full-stack cost visibility—you’ll be well-prepared to confidently populate the budgeting template provided later in this article.

Costs your AI-infrastructure budget must track

When creating your budgeting template, ensure that every item clearly aligns with one of these critical cost buckets. Collectively, they capture the complete economic footprint of your AI stack.

*Actual percentages vary based on workload type, deployment maturity, and purchasing strategy. These industry benchmarks offer a helpful sanity check when your totals seem unusual.

Template best practices:

• Assign one column per bucket: Clearly attach unit metrics, such as $/GPU-hour, $/TB/month, and $/GB egress, to easily identify and explain variances.
• Add a "hidden fees" row within each bucket: Explicitly account for retrieval charges, cross-region traffic, and premium support costs, as these often derail forecasts when left unspecified.
• Visualize your cost mix: Use stacked-area or 100%-column charts to quickly highlight when a bucket, such as networking, begins to exceed its expected guardrail of 15%.
• Tag at source: Ensure cloud resources consistently include project, pipeline phase, and environment tags, automatically categorizing each expenditure into its appropriate bucket.

This structured approach delivers clarity, accountability, and actionable insights into your AI-infrastructure spending.

How to populate the budget template when using CUDO Compute

These are the steps to transform your real-time billing data on CUDO Compute into an accurate financial forecast, proactively identifying budget leaks before Finance spots them.

Step 1: Pull a clean usage export

You have three efficient ways to extract billing data from CUDO Compute:

• Console export (CSV): Navigate to Billing Account → Invoices & Usage → Download CSV. The console reconciles usage hourly to the nearest second, ensuring that yesterday’s spend is always up to date. Read more.
• REST API (Programmatic): Fetch data and pipe directly into analytics platforms like BigQuery or Snowflake using the REST endpoint. Read more.
• Python client: Quickly access billing data with the Python client, which returns a Pandas-ready dictionary instantly. Read more.

from cudo_compute import cudo_api

usage = cudo_api.billing().usage(billing_account_id, granularity="hour")

Pro tip: Export at least 12 months of historical data at daily granularity. This provides sufficient context for seasonality without slowing your analyses.

Step 2: Tag and bucket every row

• CUDO’s usage export includes built-in metadata, including projectId, resourceType, and dataCenter. Use these keys in Excel or Google Sheets (VLOOKUP or INDEX-MATCH) to map each row directly to the six budgeting buckets we defined earlier.
• Keep each project aligned to a specific product team. Projects in CUDO serve as natural cost containers, simplifying your cost allocation process.
• Add an additional Stage column to differentiate between lifecycle phases (e.g., Dev, Training, Inference). This ensures lifecycle cost curves remain clear when pivoting data.

Step 3: Translate usage into costs

Important reminder: Verify if stopped VMs continue to accrue GPU and disk charges. CUDO maintains resource reservations until VMs are explicitly deleted.

Step 4: Model three financial scenarios

Define clear scenarios within your template to anticipate financial outcomes:

Set up dynamic lookup tables on a hidden “Lookups” tab, allowing Finance teams to easily adjust these assumptions without risking formula integrity.

Step 5: Establish sensitivity checks and alerts

Implement proactive alerts within your budgeting spreadsheet to quickly flag potential overspending:

• Idle GPU utilization: Flag VM clusters operating below 85% utilization. CUDO bills by the second; under-utilization rapidly accumulates unnecessary costs.
• Egress guardrail: Trigger alerts if monthly network egress costs surpass 15% of the Networking bucket.
• Commit-coverage KPI: Track the ratio of committed GPU hours vs. total GPU hours. Aim for a 60–80% commitment rate to optimize savings (up to 30%) based on.

Clearly visualize these metrics using conditional formatting (red, amber, green) or mini bar charts, enabling executives to spot issues in seconds.

With accurate, CUDO-specific data and proactive budget guardrails in place, you’re fully prepared for Section 5, where we’ll create the Google Sheet/Excel template, complete with interactive scenario-switching visuals. Please let me know if you'd like further adjustments or additional examples.

Building the CUDO Compute budgeting template

This section converts the concepts from Sections 1-4 into a working Google Sheet or Excel workbook that finance, engineering, and FinOps teams can all collaborate on.

Overall workbook layout

This is how the workbook should look:

How to import the raw usage data

• Google Sheets: If you are using Google Sheets, navigate to Extensions → Apps Script and set up a daily scheduled import. Your script might look like this in JavaScript:

    
    function refreshCudo() {

  const api = 'https://api.cudocompute.com/v1/billing-accounts/123/usage?granularity=daily&days=365'; 

  const json = UrlFetchApp.fetch(api, {headers:{Authorization:'Bearer YOUR_TOKEN'}}); 

  const data = JSON.parse(json); 

  // write values starting cell A2 … 

 } 

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Flatten nested fields (durationSec, pricePerUnit) into columns and insert starting from cell A2.

• Excel (Power Query): When using Excel or PowerQuery, these are the steps to take:
• Go to Data → Get Data → From Web and paste the API endpoint.
• Expand JSON lists, filter for the most recent 365 days, and load into the Raw_Usage worksheet.

These are the required columns after import:

The phase column should derive values such as 'dev', 'train', or 'infer' from resource tags.

Create Lookup tables

Set up named ranges clearly defined in the Lookups sheet:

Scenario switcher (tab Scenarios)

Implement a dropdown menu (Data Validation) to toggle scenarios:

You also need named ranges and formulas (with index-match). Define the following clearly:

Here’s how it can be done:

    
    gpu_rate := INDEX(gpu_rates!$C:$C, MATCH(selectedType & selectedRegion, gpu_rates!$A:$A & gpu_rates!$B:$B, 0))
gpu_mult := INDEX(scenario_multipliers!$B:$B, MATCH(B2, scenario_multipliers!$A:$A, 0))
price_mult := INDEX(scenario_multipliers!$C:$C, MATCH(B2, scenario_multipliers!$A:$A, 0))`

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All subsequent cost calculations reference these names, ensuring instantaneous recalculations when scenarios are switched.

Calculations in 'summary' tab

Clearly define calculated fields (Google Sheets examples):

Create a PivotTable summarizing:

• Rows: Date
• Columns: Cost Bucket
• Values: Sum of Cost
• Slicers: projectId, phase

Dashboard visuals & alerts

Provide clear visualizations and easy-to-read alerts:

• Cost mix chart: A 100% stacked-column chart clearly visualizes how Storage or Egress costs shift over time.
• Idle GPU utilization gauge (or conditional formatting), which should be formulated like this:

    
    utilization =SUMIF(Raw_Usage!resourceType,"GPU",units_used) / SUMIF(resourceType,"GPU",reserved_capacity)

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• Highlight in red if utilization falls below 85%.
• Egress guardrail bar chart: Clearly visualize egress costs as a percentage of the Networking bucket. Flag in red if above 15%.
• Commit-coverage dial: committed_GPU_hours ÷ total_GPU_hours
• Target optimal commitment rates (60–80%).

Hand-off checklist:

• Ensure the final template meets these criteria before sharing:
• Protect sheets: Lock lookup tables and formulas to prevent accidental edits.
• Automate daily refresh: Schedule via Apps Script (Google Sheets) or Power Query (Excel).
• Version-lock rates: Store snapshots of unit rates periodically for accurate invoice reconciliation and audits.
• Controlled sharing: Provide Finance users Viewer access with comment-only permissions on the Scenarios tab, ensuring control over scenario assumptions.

Navigating AI infrastructure budgets can be overwhelming, especially when hidden costs, such as idle GPUs, network egress charges, and storage overruns, lurk behind every invoice. But it doesn't have to be this way.

By adopting a clear FinOps-first strategy, embracing precise tagging, scenario-driven forecasting, and detailed cost allocations, you'll gain full control over your AI spend. You’ll not only eliminate surprise invoices but also confidently communicate costs and optimizations to stakeholders across finance, engineering, and executive teams.

Now, it's time to put theory into action. Use our ready-to-deploy AI budgeting templates designed specifically for CUDO Compute and start proactively managing your cloud expenses today.

Ready to take charge of your AI budget?

Download your free CUDO Compute budgeting template here

You're now ready with a robust, actionable budgeting template optimized for managing your AI spend on CUDO Compute.