Assessment · Governance · Cost within the governance envelope

14Workflow cost and TCO

What does each AI workflow really cost per outcome?

Every step of every workflow priced: models, retrieval, tools, review, compliance and run cost.

AutomatedStakeholder interviews 2 controls 5 engines

How we collect

AutomatedBills: AWS Cost Explorer (live connection), Azure, GCP, CUR and FOCUS exportsRuns today
AutomatedGateway telemetry: LiteLLM, Bedrock invocation logsNeeds your export or setup
AutomatedEngines price each workflow step from live model pricesRuns today
Stakeholder interviewsOwners: volumes and growthRuns today

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

What you get

Cost per step and per outcome for every workflow
Where each workload should run and what that costs
Forecast and chargeback by team

Questions we ask

How will volume grow over the next year?

We can start from

FinOps reports, FOCUS exports, chargeback models

Example output

AI spend

AICost-GovControl501

Vendor	Product	Source	Period spend	Sanctioned	Lever
Anthropic	Claude API	Usage and Cost API or gateway	Exact	Yes	Prompt caching, model routing
OpenAI	ChatGPT Plus (personal)	Card statement	Exact	No	Move to approved plan or stop
Microsoft	Copilot seats	Graph and invoice	Exact	Yes	Reclaim inactive seats

Data sources

Source	What we read	Status
Invoices, card and AP exports	AI vendors found on statements and invoices, including ones never approved. Spend is exact because it is on the statement.	Partly automated
AI gateway telemetry	LiteLLM, Cloudflare AI Gateway or AICost CostWall logs: cost per team, workload, agent and outcome.	Partly automated
Azure and AWS cost exports	Azure OpenAI and Bedrock spend from the cloud bill.	Runs today
OpenAI Usage and Costs API	Organization usage and cost by project and model, read with an Admin API key.	Wave 2
Anthropic Usage and Cost API	Token usage by model, workspace and API key, and daily cost, read with an Admin API key.	Wave 2
Google Cloud billing export	Gemini and Vertex AI cost per model from the billing export to BigQuery.	Wave 2

Built on governance engine modules

P6 LiveAI Cost Governance & FinOps

What does AI cost you, and who is accountable for it?

Value & Optimization

P13 BetaAI Cloud Architecture

Where should each AI workload run, and what does that choice cost?

Architecture & Build

AICost governance controls in this module

AICost-GovControl501Partly automated

AI spend visibility by vendor, team and workload

What it is

Every AI dollar traced to a vendor, product, team, workload and, where possible, an outcome such as a resolved ticket.

Why it is needed

Invoices say how much was spent with a vendor. They do not say which team, agent or customer caused it.

Why enterprises need it

Finance needs chargeback and leaders need cost per outcome to decide what to scale and what to stop.

What you get:Spend by team and workloadUnsanctioned spend foundCost per outcome, not just per token

Run by: AI Cost Governance and FinOps engineAI Bill Breakdown engine Token Estimator engine
Data: Invoices, card and AP exports, AI gateway telemetry, Azure and AWS cost exports, OpenAI Usage and Costs API, Anthropic Usage and Cost API, Google Cloud billing export
Evidence produced: Spend table: vendor, product, source, period spend, team, workload, sanctioned, cost per outcome.
Maps to: FinOps Framework; FOCUS cost data spec

AICost-GovControl507Runs today

Forecasts and chargeback

What it is

Monthly and quarterly AI spend forecasts by team and workload, and the chargeback or showback each team sees.

Why it is needed

Without forecasts, AI spend surprises finance each quarter and budget fights replace planning.

Why enterprises need it

CFOs want AI in the plan with ranges, not as a variance explained after the fact.

What you get:AI in the budget with rangesTeams see what they causeFewer quarter-end surprises

Run by: AI Cost Governance and FinOps engineAnnual Forecast engine Quarterly Forecast engine Scale Forecast engine
Data: Invoices, card and AP exports, AI gateway telemetry
Evidence produced: Forecast table: team, workload, last quarter, forecast range, drivers, owner.
Maps to: FinOps Framework

Frameworks


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

Stakeholders

CFOFinOpsBusiness unit leads

← 13. Agent safety and runaway control All modules 15. Cost levers and model routing →

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.

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.

14Workflow cost and TCO

How we collect

What you get

Questions we ask

We can start from

Example output

AI spend

Data sources

Executable policy

Continuous checks

Engines that run it

Built on governance engine modules

AICost governance controls in this module

Frameworks

Stakeholders

Methodology

Primary sources

Inferred values (marked with * in calculator tables)

Immediate steps you can take