Quick Answer

How to build an ROI model for private on prem generative AI

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An ROI model for private on‑prem generative AI should quantify all costs of the on‑prem platform (capex + OpEx) against measurable business value from specific use cases over 3–5 years, with payback period and NPV as primary decision metrics. The key is to model usage, benefits, and hardware utilization realistically rather than assuming “always‑on, fully utilized GPUs.”

Below is a concise, implementation‑oriented template you can adapt.

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1. Define scope, horizon, and assumptions

Start by fixing:

• Time horizon: 3–5 years (to match hardware life and depreciation).
• Scope:
  • Platform: on‑prem GPUs, storage, networking, security, observability.
  • Use cases: e.g., internal knowledge assistant, support copilot, code copilot, etc.
• Core assumptions:
  • Number of users and monthly queries per user.
  • Average tokens per query/response (or average GPU seconds per request).
  • Salaries (fully loaded cost per FTE), margin per deal, etc.
  • Discount rate (for NPV) and target payback period.

This “Assumptions” tab drives the rest of the model.

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2. Model total cost of ownership (TCO) for on‑prem

2.1 Capex

Include:

• GPU servers (e.g., 2–4 nodes with specified GPUs).
• Storage (NVMe for hot data, NAS/object for corpora and backups).
• Networking upgrades (switches, links, firewalls if required).
• Data center fit‑out if incremental (racks, PDUs, cooling modifications).

For each hardware category:

• Capex_i
• Useful life (years_i)

Annualized capex_i = Capex_i / useful_life_i Monthly capex_i = Annualized capex_i / 12

Sum across all hardware to get monthly “hardware amortization.”

2.2 Operating expenses

Include:

• Power and cooling:
  • Estimate kW per rack × hours × local energy cost.
• Maintenance and support contracts:
  • Hardware support, extended warranties.
• Software and platform licenses:
  • Enterprise Linux, Kubernetes support, observability, security tools, vector DB licenses if not open‑source.
• Personnel:
  • Fractional FTEs for:
    • MLOps/infrastructure.
    • ML engineer / data scientist.
    • Data engineer.
    • Security/compliance.
  • Use fully loaded annual cost × FTE share.

On‑prem OpEx_monthly = power + cooling + maintenance + platform licenses + personnel_share

Total on‑prem monthly TCO = hardware_amortization_monthly + OpEx_monthly

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3. Build a “cloud reference” cost curve

To check whether on‑prem makes sense, you need a comparable cloud scenario:

• Estimate:
  • Monthly tokens or GPU hours needed for your use cases.
• Apply:
  • Per‑token or per‑hour prices from your shortlisted cloud LLM or GPU providers.
• Add:
  • Storage, networking, and managed service fees (vector DB, gateways, observability).

Cloud_cost_monthly = (tokens_monthly × price_per_token) + storage + networking + managed_services

Use this as a reference line; your on‑prem ROI is more credible if you can show where on‑prem TCO undercuts equivalent cloud cost at your usage level.

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4. Quantify benefits per use case

Create a separate section for each major use case and calculate annual benefits.

4.1 Productivity savings

Example framework:

• Baseline:
  • Tasks/month_baseline
  • Minutes per task_baseline
• With AI:
  • Minutes per task_AI
• Time saved per task = baseline − AI
• Hours saved per month = (time_saved_per_task × tasks/month) / 60
• Monetary benefit = hours_saved_per_month × hourly_cost

Apply this per role (e.g., support, sales, operations), then annualize.

4.2 Revenue uplift

Example:

• Leads/month or opportunities/month affected by AI.
• Baseline conversion rate vs AI‑assisted conversion rate.
• Deal value and margin.

Revenue_uplift_per_year = (conv_rate_AI − conv_rate_baseline) × leads_per_year × avg_margin

4.3 Risk and quality

Quantify where feasible:

• Reduced error/rework:
  • Errors avoided × cost per error.
• Faster cycle times:
  • Days/month reduced × value of earlier revenue or lower WIP.
• Compliance and risk:
  • Use conservative estimates (e.g., reduced likelihood or impact of incidents vs pre‑AI baseline) only if you can justify them.

Total annual benefit = productivity + revenue + risk/quality benefits across all use cases.

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5. Combine costs and benefits into an ROI model

For each year in your 3–5 year horizon:

1. Compute total costs:
   • On‑prem TCO_year = 12 × Total on‑prem monthly TCO + any one‑off project costs that year.
2. Compute total benefits:
   • Sum of annual benefits from all use cases.
3. Calculate:
   • Net benefit_year = benefits_year − costs_year
   • Cumulative net benefit over time.
   • Simple ROI_year = (benefits_year − costs_year) / costs_year

If you want NPV:

• NPV = Σ (net_benefit_year / (1 + discount_rate)^year) − initial_capex not already amortized

Payback period:

• The first year (or month, if modeled monthly) where cumulative net benefit ≥ 0.

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6. Incorporate utilization and scale scenarios

On‑prem economics are highly sensitive to GPU utilization and workload growth. Create scenarios:

• Low utilization:
  • GPUs at 20–30% utilization (few use cases, low adoption).
• Target utilization:
  • 60–70% average utilization (healthy multi‑use‑case platform).
• High growth:
  • Usage growing 2–3× over 2–3 years.

For each scenario:

• Adjust tokens/month or GPU hours and the number of use‑case benefits.
• Recompute cloud_cost_monthly vs on‑prem TCO.
• Show where on‑prem becomes cheaper than cloud and how ROI changes.

This lets you see, for example, that:

• At low utilization, cloud may remain cheaper indefinitely.
• At high, steady usage, on‑prem can break even in 6–18 months and outperform cloud over the remainder of the hardware life.

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7. Build a summary dashboard (for execs)

Summarize your ROI model in a simple view:

• Inputs:
  • Number of users, queries/month, tokens/query.
  • Capex, OpEx, and FTE assumptions.
• Outputs:
  • Annual benefits vs costs (stacked bar).
  • Payback period (months).
  • 3–5 year ROI (%) and NPV.
  • Cloud vs on‑prem cost curves across usage.

This lets leadership quickly compare “cloud only,” “on‑prem private,” and “hybrid” options using consistent assumptions.

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8. Practical tips for credible ROI

• Start with 1–3 high‑impact use cases; don’t assume benefits across the entire company until proven.
• Use conservative, defendable assumptions (e.g., count only a portion of time savings as actually monetized).
• Run sensitivity analysis:
  • What if adoption is 50% of plan?
  • What if infra costs are 20% higher?
• Treat the on‑prem platform as shared infrastructure:
  • Attribute cost to multiple use cases to avoid over‑burdening the first project.

Using this structure, you can build a spreadsheet that gives a transparent, defensible ROI view for investing in private on‑prem generative AI, and compare it directly to staying in the cloud or choosing a hybrid approach.

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