AI Infrastructure Tokenomics Calculator
Version 1.3 · Last Updated 8 July 2026
EY - NVIDIA - DELL Alliance Initiative
AI Infrastructure Tokenomics Calculator
A directional planning tool that helps Solution Architects model the economics of enterprise agentic AI —
framing the cloud vs. on-premises conversation before the bill arrives.
15
Industry Sectors
10
Use Cases Each
6
Analysis Steps
5
Token Parameters
What is this?
A directional TCO tool for enterprise AI infrastructure decisions.
Models token economics across agentic AI use cases
Scores token intensity via a 5-parameter heat map
Compares cloud API vs. Dell AI Factory on-prem TCO
v1.3 · 8 July 2026 · Internal use only
Why was it built?
Enterprise AI costs become a surprise invoice when architecture decisions are deferred.
Standard tools use list-price cloud rates — overstating cloud cost
On-prem OpEx is routinely understated (headcount, licensing omitted)
Breakeven ignores ramp-up time and Year 1 underutilisation
How to use it
Six sequential steps — work through them in order:
1 · SectorChoose industry, load 10 use cases
2 · VolumesEdit token counts; use estimator
3 · Heat MapReview intensity; select/deselect
4 · PricingSet rates; apply enterprise discount
5 · BreakevenSet horizon, ramp-up, utilisation
6 · OutputReview TCO; select config; recalculate
Reading the results
Output is directional — right order of magnitude, not audit-grade.
Open the conversation — not close a CapEx decision
Test the discount slider — it often flips the recommendation
Find the tipping point — Red-rated use cases are on-prem candidates
Validate before acting — engage specialists for formal proposals
Ready to begin? Six steps. Directional output. Built for client conversations.
Validate with specialist teams before use in formal proposals.
AI Infrastructure Tokenomics Calculator
1
Select Sector
2
Use Case Volumes
3
Token Heat Map
4
Token Pricing
5
Breakeven Window
6
Recommendation & Analysis
Step 1 of 6
SELECT YOUR SECTOR
Choose an industry sector to load the top 10 pre-configured AI use cases. All fields are fully editable to match your organisation's workload.
Step 2 of 6
USE CASE VOLUMES
Edit use case names, daily agentic interaction volumes, and average token counts per interaction. Click Estimate tokens → on any row to open the token estimator and calculate avg input / output tokens from 5 interaction parameters.
—
All fields editable · Click "Estimate tokens" to auto-calculate input & output tokens
Use Case
Agentic Interactions /Day ?
Avg Input Tokens /Interaction ?
Avg Output Tokens /Interaction ?
Est. Total Tokens /Day ?
TOTAL (ALL USE CASES)
—
—
—
Total Input Tokens / Day
—
all use cases combined
Total Output Tokens / Day
—
all use cases combined
Total Tokens / Month
—
× 30 days
Annualised Token Volume
—
estimated annual total
Step 3 of 6
TOKEN INTENSITY HEAT MAP
All use cases are scored across 5 technical parameters that drive token consumption. Hover column headers for definitions. Use the checkboxes to include or exclude use cases from all downstream calculations — the heat map always shows all rows.
—
High
Medium
Low
In
Use Case
Reasoning Depth
Context Statefulness
Tool Invocation & Routing
Branching Factor
Task Determinism
Overall Token Intensity
How to read this
■High: These use cases will drive the majority of your token volume. Prioritise accuracy in their volume estimates.
■Medium: Important at scale but individually manageable. Consider batching where feasible.
■Low: Low burden per interaction. High-volume low-intensity tasks still aggregate — check daily volumes.
Step 4 of 6
TOKEN COST INPUTS
Enter cloud LLM API pricing for input and output tokens separately. Use quick-fill benchmarks or enter a custom rate.
📥
Input Token Cost
Range: $0.15 (GPT-4o mini) → $5.00 (Claude Opus 4.7) · Prices as of May 2026
Quick-fill benchmarks
📤
Output Token Cost
Output tokens are typically 3–5× more expensive per token than input
Quick-fill benchmarks
Token Usage Cost Preview — Based on Current Volumes
Enterprise / Committed-Use Cloud Discount
?
0%10%20%30%40%50%60%70%
0%
Effective input: —
Effective output: —
⚠️ Why this matters for the TCO conclusion
The default comparison uses published list prices — the same rates a startup pays for their first API call. Enterprises operating at the token volumes that justify a $480K–$2M GPU cluster have invariably negotiated committed-use discounts. At 50% off list price, a scenario that shows on-prem saving $1.2M over 3 years often inverts to cloud being $800K cheaper. Always obtain your cloud account team's enterprise rate before drawing a final conclusion.
Define the investment horizon for comparing cloud vs. on-premises economics.
Investment Horizon
1 Year
2 Years
3 Years
4 Years
5 Years
Selected: 3 Years (36 months)
Deployment Realism Adjustments
Deployment Ramp-Up Period
?
6 mo
Both cloud and on-prem costs accumulate for 6 months before any workloads migrate.
Year 1 Platform Utilisation
?
35%
Volume above Year 1 capacity continues to cloud at effective API rates — adding to on-prem cost.
Year 2+ Utilisation (steady state)
70 %
Steady-state utilisation assumption once platform is fully adopted. Default 70% follows standard GPU cluster benchmarking.
⚠️ Why standard breakeven calculations tend to be optimistic
Most TCO analyses assume the on-prem system handles 100% of workloads from day one. In practice: hardware procurement takes 3–6 months, installation and security certification add another 2–4 months, and Year 1 utilisation rarely exceeds 30–40% as use cases onboard. A scenario that looks like breakeven at month 18 often realistically breaks even at month 28–34 — sometimes beyond a 3-year window entirely, which can reverse the recommendation.
The chart on the next page shows the adjusted breakeven curve accounting for your ramp-up period and Year 1 utilisation. The shaded zone marks the ramp period where both cost streams run simultaneously with no on-prem production.
