Guides → Playground & Guide → TCO Complete - 7-Step Procurement-Grade Wizard for AI Workloads
Meet Marcus Chen. Director of FP&A preparing a multi-year AI procurement case. "I need a TCO model the procurement committee will accept - workload-specific, with sensitivity analysis and a defensible NPV."
🔥 Engineering hands me a $30K/mo number. Procurement asks for a 36-month TCO with NPV, payback, and risk-adjusted scenarios. Wide gap.
Quick gives a board-ready number. Complete gives a contract-ready model. TCO Quick estimates total cost in 90 seconds with 5 inputs - perfect for board updates. TCO Complete is the procurement-grade version: 7 steps, calc handoffs, sensitivity analysis, NPV/IRR/Payback, persona-tuned executive synthesis. Use it when the contract is real.
Marcus's case: $30K/mo inference grows to $85K loaded TCO (Quick). But procurement wants 36-month NPV, best/expected/worst trajectories, and a defensible production-readiness uplift across security + compliance + observability. Quick can't get there. Complete can. The wizard composes the existing pure calcs (inference economics, agentic workflow, RAG pipeline, etc.) into a single procurement document.
7 steps, each feeding the next. (1) Context - workload + vertical + cloud + persona. (2) Inference economics - model + scale + caching + batching. (3) Capability stack - composable: retrieval, voice, agentic, fine-tuning, multimodal, evaluation. (4) Scale + finance trajectory - growth model + budget cap + NPV/IRR/Payback. (5) 6-pillar uplift - security + compliance + observability + PII + HITL + cost controls. (6) ROI sensitivity - tornado chart across volume, growth, pricing, HITL, PII, compliance. (7) Synthesis - persona-tuned executive plan (CFO/CTO/Founder/PM).
Here are the inputs that move the result the most. Play with the sliders and check it out. The number updates live.
Discounts growing monthly benefits over the horizon back to today’s dollars, minus the upfront investment. Investment and benefit fixed at defaults.
💡NPV = present value of growing monthly benefits over the horizon − upfront investment. Positive means it pays off.
Same calculator, three team sizes. Click a tab to see how the numbers shift.
Mid-stage SaaS adding AI to existing product. $250K capex + $30K/mo inference at start, growing 5%/mo. $50K/mo benefit (cost displacement + revenue lift). 12% discount rate. NPV at 36mo should clear $500K with payback ~15mo.
Healthy range: NPV positive at 36mo, payback <18mo
Pre-revenue startup. Higher discount rate (18%) reflects WACC. Shorter horizon (24mo) reflects pivot risk. Lower investment but tighter benefit assumptions. Use this to stress-test.
Healthy range: Payback within horizon
Mature enterprise. 60mo horizon. Lower growth rate (3%/mo) because mature scale. Lower discount rate (8% WACC). Heavy capex, large benefit stream. NPV should clear $5M.
Healthy range: NPV >$5M, IRR >25%
Same calculator, different applications. Sizes above, workloads here. Pick the one that looks like yours.
Pre-loaded scenarios for the most common applications. Click a tab to see realistic numbers, then hit "Try this scenario" to load it into the calculator above.
Healthcare AI for documentation automation. HIPAA + audit + SOC 2 push 6-pillar uplift to ~40% of baseline. Step 5 makes this visible. Procurement committee needs the line-item breakdown.
Healthy range: NPV positive with 6-pillar uplift fully on
Fintech application processing transactions + PII. PII redaction tooling, key management, audit logging are significant TCO lines. ROI sensitivity tornado will show PII volume as a top-3 driver.
Healthy range: PII pillar drives 15-25% of total uplift
Agency white-labeling AI to clients. Multi-tenant. Higher growth rate (client acquisition), shorter horizon, moderate capex. Cost-controls pillar dominates (per-client metering).
Healthy range: Payback <12mo
Open the full calculator. Pick a model, enter your tokens, see per-call, daily, monthly, and annual cost.
🚀 Open the full calculator →Build a defensible AI TCO model in 15 minutes. Workload × vertical × cloud aware, 6-pillar uplift, NPV/IRR/Payback, persona-tuned executive plan.
| Input | Default | Typical ballparks |
|---|---|---|
horizonMonths
moves the needle
|
36 | — |
growthRatePctPerMo
moves the needle
|
5 | — |
discountRatePct
moves the needle
|
12 | — |
investmentUsd
|
250,000 | — |
benefitMonthlyUsd
|
50,000 | — |
Ballparks are broad industry starting points (sourced ranges; * = rough estimate) — your result gets more accurate as you replace them with measured numbers.
Try them live in the calculator;
API & agent users get the same data from the MCP resource aicost://input-reference/tco-complete.
The 7-step output is a procurement document, not a number. You get a multi-page executive plan, NPV/IRR/Payback, sensitivity tornado, and 6-pillar uplift breakdown.
Persona-tuned synthesis. CFO sees finance front-and-center (NPV, payback, scenarios). CTO sees tech debt and capability mix. Founder sees runway impact and risk premium. PM sees roadmap dependencies.
Calc handoffs are first-class. Step 2 calls the inference economics calc. Step 3 composes 6 capability calcs (retrieval, voice, agentic, fine-tuning, multimodal, evaluation). Step 4 invokes the trajectory + NPV engines. No double-counting. Each step's output becomes the next step's input.
ToolsInfo drill-downs are linked. Each pillar in the 6-pillar uplift links to ToolsInfo for vendor selection. You pick the vendor; we model the cost.
Honest limitations. Every model is wrong; some are useful. Where this one falls short:
For these, use: Cost Calculator for inference detail. Concentration Risk for explicit risk modeling. TCO Quick if you need a 90-second board number.
Cost isn't the only dimension. Click any constraint to see how recommendations change.
The wizard's job is to bridge engineering's monthly inference number with procurement's multi-year defensible NPV. Both are correct at their scope; the gap is what TCO Complete fills.
Hallucination is a quality + risk concern. The wizard sizes the budget for evaluation infrastructure (Step 3 Evaluation capability) and for human-in-the-loop review (Step 5 HITL pillar). Both are cost lines.
Step 5's compliance pillar auto-enables based on industry context. SOC 2 ~10%, HIPAA + SOC 2 ~25%, PCI + HIPAA + SOC 2 ~40%.
Privacy is a TCO line item, not an afterthought. The PII pillar models it explicitly.
Latency tooling shows up as observability pillar in Step 5 and possibly edge-deployment in Step 3.
Step 6's pricing sensitivity sweep quantifies the cost of vendor price hikes. Multi-vendor capability buys insurance against this.
Eval pipeline, drift monitoring, prompt versioning, A/B testing - all show up as line items. The wizard prevents double-counting by structuring each capability separately.
Tradeoff analysis is where most AI projects go sideways. Talk to a CFO-grade AI cost analyst →
The gaps we just listed are real, and they are the expensive ones: your actual prompts, your switching cost, your MLOps overhead. An AICost expert spends the hour on your AI and cloud costs, not a generic playbook. You leave with a written report: the way forward, in 30 days of concrete steps.
Not sure yet? The $39 AICost Blueprint credits toward a Session, and the Session fee credits toward any plan. You never pay twice for the same ground. See all pricing →
You have seen the shape of it. Open the calculator with your model, your tokens, your volume.
🚀 Open the full calculator →5 questions, 90 seconds. Use when contract isn't yet on the table.
Self-host vs API economics →Inform Step 3 inference economics decision.
Outcome-priced vendor vs build →Inform Step 1 workload framing.
12-month detailed forecast →Step 4 alternative for shorter horizons.
Author: Subu Vdaygiri, Founder & CEO of CloudIntelligence.ai. 17 years Fortune 100 (Ingram Micro, Siemens). Wharton CTO program · Kellogg CPO program · 10× AWS+Azure certified.
Why this matters: pricing for major vendors has dropped 40-90% in the last 24 months. A budget set 12 months ago is probably wrong by 30%+.
View 3-year history for →
Last-verified date is the most recent successful daily snapshot
(aicost_pricing_snapshots) or, when no snapshot exists yet,
the latest successful crawler run (aicost_crawler_runs).
10 of 10
vendors are currently verified. Aggregator services (TokenCost, AI Pricing Guru, etc.)
are not listed.
Derived from industry conventions, not directly published by the vendor. Typical conventions: cached input = 10% of base (90% off), Batch API = 50% of base (50% off).
| Vendor / Model | Field | Why it’s inferred |
|---|---|---|
| Anthropic — Claude Sonnet 4.6 | cachedInput |
Derived at 10% of input rate — Anthropic publishes 90% cache-hit discount on this tier. |
| Anthropic — Claude Sonnet 4.5 | cachedInput |
Derived at 10% of input rate; same 90% cache-hit convention as Sonnet 4.6. |
| Anthropic — Claude Sonnet 4.5 | batchInput |
Derived at 50% of standard input — Anthropic documents uniform 50% Batch discount. |
| Anthropic — Claude Sonnet 4.5 | batchOutput |
Derived at 50% of standard output — Anthropic documents uniform 50% Batch discount. |
| Anthropic — Claude Haiku 4.5 | cachedInput |
Derived at 10% of input rate — Anthropic 90% cache-hit discount convention. |
| OpenAI — GPT-5.4 Mini | cachedInput |
Derived at 10% of input — OpenAI documents automatic 90% discount on cache hits across GPT-5.x tier. |
| OpenAI — GPT-5.4 Nano | cachedInput |
Derived at 10% of input — OpenAI 90% cache-hit convention. |
| OpenAI — GPT-5.4 Nano | batchInput |
Derived at 50% of input — OpenAI Batch API uniform 50% discount. |
| OpenAI — GPT-5.4 Nano | batchOutput |
Derived at 50% of output — OpenAI Batch API uniform 50% discount. |
| OpenAI — GPT-5.4 Pro | cachedInput |
Derived at 10% of input — OpenAI 90% cache-hit convention. |
| OpenAI — GPT-5.4 Pro | batchInput |
Derived at 50% of input — OpenAI Batch API uniform 50% discount. |
| OpenAI — GPT-5.4 Pro | batchOutput |
Derived at 50% of output — OpenAI Batch API uniform 50% discount. |
| OpenAI — GPT-5.2 | cachedInput |
Derived at 10% of input; no residency uplift. |
| OpenAI — GPT-5.2 | batchInput |
Derived at 50% of input. |
| OpenAI — GPT-5.2 | batchOutput |
Derived at 50% of output. |
| OpenAI — GPT-5 | cachedInput |
Derived at 10% of input. |
| OpenAI — GPT-5 | batchInput |
Derived at 50% of input. |
| OpenAI — GPT-5 | batchOutput |
Derived at 50% of output. |
| OpenAI — GPT-5.5 Pro | cachedInput |
Derived at 10% of input — OpenAI does not publish a cached rate for *-pro models; using the family convention. |
| OpenAI — GPT-5.5 Pro | batchInput |
Derived at 50% of input. |
| OpenAI — GPT-5.5 Pro | batchOutput |
Derived at 50% of output. |
| OpenAI — GPT-5.2 Pro | cachedInput |
Derived at 10% of input — pro-tier convention. |
| OpenAI — GPT-5.2 Pro | batchInput |
Derived at 50% of input. |
| OpenAI — GPT-5.2 Pro | batchOutput |
Derived at 50% of output. |
| OpenAI — GPT-5.1 | batchInput |
Derived at 50% of input. |
| OpenAI — GPT-5.1 | batchOutput |
Derived at 50% of output. |
| OpenAI — GPT-5 Pro | batchInput |
Derived at 50% of input. |
| OpenAI — GPT-5 Pro | batchOutput |
Derived at 50% of output. |
| OpenAI — GPT-5 Nano | cachedInput |
Derived at 10% of input. |
| OpenAI — GPT-5 Nano | batchInput |
Derived at 50% of input. |
| OpenAI — GPT-5 Nano | batchOutput |
Derived at 50% of output. |
| Google — Gemini 3 Flash | cachedInput |
Derived at 10% of input — Google caching discount convention ~90%. |
| Google — Gemini 3.1 Flash-Lite | cachedInput |
Derived at 10% of input — Google caching convention. |
| Google — Gemini 3.1 Flash-Lite | batchInput |
Derived at 50% of input — Google Batch API uniform 50% discount. |
| Google — Gemini 3.1 Flash-Lite | batchOutput |
Derived at 50% of output — Google Batch API uniform 50% discount. |
| Google — Gemini 2.5 Pro | cachedInput |
Derived at 10% of input. |
| Google — Gemini 2.5 Flash | cachedInput |
Derived at 10% of input. |
| Google — Gemini 2.5 Flash-Lite | cachedInput |
Derived at 10% of input — Google caching convention. |
| Google — Gemini 2.5 Flash-Lite | batchInput |
Derived at 50% of input — Google Batch API uniform 50% discount. |
| Google — Gemini 2.5 Flash-Lite | batchOutput |
Derived at 50% of output — Google Batch API uniform 50% discount. |
| Google — Gemini 2.0 Flash | cachedInput |
Derived at 25% of input per Google 2.0 family caching rates. |
| Google — Gemini 2.0 Flash | batchInput |
Derived at 50% of input — Google Batch API uniform 50% discount. |
| Google — Gemini 2.0 Flash | batchOutput |
Derived at 50% of output — Google Batch API uniform 50% discount. |
| Google — Gemini 2.0 Flash-Lite | cachedInput |
Derived at 10% of input — Google caching convention. |
| Google — Gemini 2.0 Flash-Lite | batchInput |
Derived at 50% of input — Google Batch API uniform 50% discount. |
| Google — Gemini 2.0 Flash-Lite | batchOutput |
Derived at 50% of output — Google Batch API uniform 50% discount. |
| xAI — Grok 4 (legacy) | cachedInput |
Extrapolated at 25% of base. |
Pricing is cross-verified against the
LiteLLM community registry
when available. Daily snapshots are kept in aicost_pricing_snapshots;
every change is logged to aicost_price_changelog with old & new
values for full audit trail. Read the full methodology →