Assessment · Governance · Run continuously

18Executable policy

How do our rules actually run, not just sit in a document?

Cost and governance rules compiled into policy your own gateway or cloud enforces. AICost stays out of the request path unless you choose the enforcing proxy.

AutomatedStakeholder interviewsUses your existing assessments 3 controls 3 engines

How we collect

AutomatedGateway and cloud configs read to see what is enforced todayNeeds your export or setup
Stakeholder interviewsDecisions, not policies: 25 questions on data, agents, tools and oversightRuns today
Uses your existing assessmentsPolicies from your GRC tool turned into rulesPlanned

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

What you get

Gap between written policy and enforced policy

Questions we ask

Which gateway or cloud console do you use?

We can start from

Policy documents you already have; we turn them into decisions and rules

Data sources

Source	What we read	Status
AI gateway telemetry	LiteLLM, Cloudflare AI Gateway or AICost CostWall logs: cost per team, workload, agent and outcome.	Partly automated
Questionnaire	The short interview for what data cannot show: purpose, owners, decisions AI informs, regulations in scope.	Runs today
Microsoft Graph and Entra ID	Service 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

P6 LiveAI Cost Governance & FinOps

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

Value & Optimization

P14 PlannedPrivacy, Confidentiality & IP

Whose data trains what, and who owns the output?

Assess & Govern

AICost governance controls in this module

AICost-GovControl502Runs today

Budgets, caps and spend guardrails

What it is

Monthly budgets, daily hard limits, per-run caps, retry and context limits, and approval before spend above a threshold.

Why it is needed

AI cost moves with usage. Without caps, one loop or one long-context workload can exceed a month of budget.

Why enterprises need it

Enterprises want predictable AI bills and the ability to stop runaway spend without stopping the business.

What you get:Predictable monthly AI spendRunaway workloads stopped automaticallyLimits enforced where traffic flows

Run by: AI Cost Governance and FinOps engineCost Firewall engine Budget Planner engine Agent Cost Reserve engine Retry Waste engine
Data: AI gateway telemetry, Questionnaire
Evidence produced: Guardrail table: workload, monthly budget, daily limit, per-run cap, enforcement point, last breach.
Maps to: FinOps Framework; NIST AI RMF: Manage

AICost-GovControl402Runs today

Limits on what agents can do

What it is

Rules for agent actions: which tools, which data, how many steps, how much spend, which actions need a person, and a tested stop switch.

Why it is needed

Agents act, not only answer. A loop or a wrong tool call can change records or run up a bill in minutes.

Why enterprises need it

Before giving agents more autonomy, teams need hard limits that hold even when the model misbehaves.

What you get:Bounded actions and spend per runHuman approval on consequential stepsA stop switch that is tested

Run by: Agents, MCP and Autonomous Actions engineAgent Access Limits engine Cost Firewall engine Agent Loop engine Cost per Agent Run engine
Data: AI gateway telemetry, Microsoft Graph and Entra ID, Questionnaire
Evidence produced: Agent limits table: agent, tools allowed, data allowed, max steps, max cost per run, approval points, stop test date.
Maps to: NIST AI RMF: Manage; OWASP Top 10 for LLM: excessive agency

AICost-GovControl304Runs today

Data residency and model origin

What it is

Where each model runs and where its provider is based, matched to your residency and sovereignty rules.

Why it is needed

Some data cannot leave a region, and some buyers will not accept models from certain jurisdictions.

Why enterprises need it

Public sector, health and finance buyers ask for this in every security review.

What you get:Allowed regions per data classModel origin risk knownApproved routes for sensitive work

Run by: Privacy Decision engineAI Architecture Decision engineModel Origin Risk engine Local or Cloud engine
Data: Questionnaire, AI gateway telemetry
Evidence produced: Residency table: system, data class, model, provider country, processing region, allowed or blocked.
Maps to: GDPR transfers; Sector sovereignty rules

Frameworks


GDPR and CCPA/CPRA	Lawful basis, minimization, transfers, retention, explanation duties
OWASP Top 10 for LLM	Prompt injection, data leakage, excessive agency, supply chain
FinOps Framework and FOCUS	Cost visibility, allocation, budgets, forecasting and unit economics for AI

Stakeholders

AI platform ownerCISOCompliance

← 17. ROI and unit economics All modules 19. Continuous monitoring and incidents →

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.

18Executable policy

How we collect

What you get

Questions we ask

We can start from

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