Assessment · Governance · Cost within the governance envelope

17ROI and unit economics

Is each workflow worth what it costs?

Value and cost per resolved case, document or task, and seats that are actually used.

AutomatedStakeholder interviews 2 controls 4 engines

How we collect

  • AutomatedSeat usage from Graph and licence data from Partner Center and Pax8Runs today
  • AutomatedOutcomes from workflow logsNeeds your export or setup
  • Stakeholder interviewsOwners: value per outcomeRuns today

Status labels show what runs today, what needs an export or setup from you, and what is planned.

What you get

  • Hard dollars, hours, outcomes and risk reduced, reported separately
  • Unused seats and duplicate licences to reclaim
  • Cost per outcome (needs a ticket or case count)

Questions we ask

  • What is one resolved case worth to you?

We can start from

Business cases, seat reports

Data sources

SourceWhat we readStatus
AI gateway telemetryLiteLLM, Cloudflare AI Gateway or AICost CostWall logs: cost per team, workload, agent and outcome.Partly automated
Invoices, card and AP exportsAI vendors found on statements and invoices, including ones never approved. Spend is exact because it is on the statement.Partly automated
QuestionnaireThe short interview for what data cannot show: purpose, owners, decisions AI informs, regulations in scope.Runs today
Microsoft Graph and Entra IDService principals, OAuth grants, app registrations, sign-ins to AI apps, Copilot usage reports. Read-only scopes, metadata only.Runs today

Built on governance engine modules

P3 PlannedUse Cases, ROI & Business Value

Which AI use cases are worth doing, and what are they worth?

Value & Optimization
P16 PlannedAdoption, Training & Change

Are people actually using it, safely, and measurably better?

Value & Optimization
P15 LiveLicensing, Procurement & Sprawl

What are you paying for that nobody uses, or double-buying?

Value & Optimization

AICost governance controls in this module

AICost-GovControl504Runs today
Value and ROI per AI use
What it is

Cost per outcome, total cost of ownership and payback for each AI use, measured against the result it was approved for.

Why it is needed

AI that costs more than the work it replaces should be changed or stopped, and that needs numbers.

Why enterprises need it

Boards ask what AI returns. Product owners need proof to keep funding.

What you get:Keep, change or stop decisions on evidenceCost per outcome trackedBusiness cases checked after launch
Run by
AI Value and Workflow engineTotal Cost and ROI engineAgent TCO and ROI engineSupport Deflection ROI engineAI Margin engine
Data
AI gateway telemetry, Invoices, card and AP exports, Questionnaire
Evidence produced
Value table: system, outcome measure, cost per outcome, baseline, payback, decision.
Maps to
NIST AI RMF: Measure; FinOps for AI
AICost-GovControl505Runs today
Licenses and seats that are used
What it is

Paid AI seats (Copilot, ChatGPT Team, Claude Team, Gemini) matched to real usage, with reclaim and right-size actions.

Why it is needed

Seat-based AI is easy to buy and hard to track. Unused seats are pure waste.

Why enterprises need it

IT and finance want to pay only for seats people use and move licenses to those who need them.

What you get:Unused seats reclaimedPlans sized to real usageAdoption gaps visible
Run by
License and Seat Value engineAI Adoption and Enablement enginePlan Overage enginePlan Tier Compare engine
Data
Microsoft Graph and Entra ID, Invoices, card and AP exports
Evidence produced
Seat table: product, seats paid, seats active in 30 and 90 days, cost per active user, action.
Maps to
FinOps Framework

Frameworks

FinOps Framework and FOCUSCost visibility, allocation, budgets, forecasting and unit economics for AI

Stakeholders

CFOBusiness unit leads
← 16. Budgets, caps and spend guardrails All modules 18. Executable policy →
📖 Data sources & methodology 172 text models · 9 embeddings · 44 vision · 62 audio · 8 vector DBs across 10 vendor pages · last verified 2026-10-18

Methodology

  • All prices are USD per 1 million tokens, current as of 2026-10-18.
  • Vendor-published values have no mark. Inferred/extrapolated values are marked with * and listed below.
  • Batch API discounts are 50% off standard rates across providers that offer Batch mode.
  • Prompt caching discounts vary by provider (typically 80-90% off cached input tokens).
  • Regional data-residency surcharges (Anthropic 1.1x, OpenAI 1.1x, Google regional tiers) are NOT included in base rates.
  • Long-context pricing tiers apply when input exceeds model threshold.
  • Embedding prices are input-only (no output tokens generated).

Primary sources

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.

Anthropic
2026-10-18
https://www.anthropic.com/pricing
Daily snapshot since Sep 2023 · 704 days captured
Anthropic Docs
2026-10-18
https://platform.claude.com/docs/en/about-claude/pricing
Daily snapshot since Sep 2023 · 704 days captured
OpenAI
2026-10-18
https://openai.com/api/pricing/
Daily snapshot since Sep 2023 · 705 days captured
Google AI
2026-10-18
https://ai.google.dev/gemini-api/docs/pricing
Daily snapshot since Dec 2023 · 680 days captured
Google Vertex
2026-10-18
https://cloud.google.com/vertex-ai/generative-ai/pricing
Daily snapshot since Dec 2023 · 680 days captured
DeepSeek
2026-10-18
https://api-docs.deepseek.com/quick_start/pricing
Daily snapshot since May 2024 · 619 days captured
xAI
2026-10-18
https://x.ai/api
Daily snapshot since Nov 2024 · 537 days captured
Mistral
2026-10-18
https://mistral.ai/pricing
Daily snapshot since Dec 2023 · 678 days captured
Cohere
2026-10-18
https://cohere.com/pricing
Daily snapshot since Sep 2023 · 704 days captured
Voyage AI
2026-10-18
https://docs.voyageai.com/docs/pricing

Inferred values (marked with * in calculator tables)

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.
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 →