Assessment · Governance · Assess and know

2Workflow map and owners

Which workflows use AI, who owns them and what does each step do?

Each AI system mapped to the business workflow it serves, its steps and a named owner.

AutomatedStakeholder interviewsUses your existing assessments 3 controls 3 engines

How we collect

  • AutomatedGateway and cloud logs group calls by app and workloadNeeds your export or setup
  • Stakeholder interviewsStakeholder interviews per workflow: steps, systems, decisions, ownerRuns today
  • AutomatedWorkflow templates from the AICost catalog, adapted per customerRuns today
  • Uses your existing assessmentsProcess maps and RACI from your existing toolsPlanned

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

What you get

  • Workflow register: steps, systems, models, owner, data class
  • Template reuse: one template, many customer adapters
  • Gaps: AI with no owner or purpose

Questions we ask

  • Walk us through the workflow step by step.
  • Which decisions does the AI make or suggest?

We can start from

Process maps, RACI charts, model registries

Data sources

SourceWhat we readStatus
QuestionnaireThe short interview for what data cannot show: purpose, owners, decisions AI informs, regulations in scope.Runs today
AI gateway telemetryLiteLLM, Cloudflare AI Gateway or AICost CostWall logs: cost per team, workload, agent and outcome.Partly automated

Built on governance engine modules

P10 PlannedWorkflow Integration & Automation

Can AI workflows run safely against your systems of record?

Architecture & Build
P3 PlannedUse Cases, ROI & Business Value

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

Value & Optimization

AICost governance controls in this module

AICost-GovControl102Partly automated
Purpose and intake approval
What it is

Each new AI use states the business purpose, the users, the data it needs and the expected cost before it is approved.

Why it is needed

Without a stated purpose there is nothing to judge access, data use or spend against, so every request looks reasonable.

Why enterprises need it

Teams want AI quickly. A light intake with clear rules lets low-risk uses through in a day and slows only the ones that need it.

What you get:Faster approvals for low-risk usesCost estimate attached before money is committedA record of why each system exists
Run by
Governance Assessment wizardAI Workflow Integration engineTotal Cost and ROI engineToken Estimator engine
Data
Questionnaire
Evidence produced
Intake record: purpose, users, data classes, expected monthly cost, risk tier, approver, decision date.
Maps to
NIST AI RMF: Govern, Map; ISO/IEC 42001: AI system impact assessment
AICost-GovControl103Partly automated
Named owners and accountability
What it is

Every AI system has a business owner, a technical owner and a person who accepts the remaining risk and the budget.

Why it is needed

Shared committees without named owners leave findings open and bills unexplained.

Why enterprises need it

Boards and auditors expect a clear chain from the board to the person who can switch a system off. Three lines of defense need names, not teams.

What you get:Findings close because someone owns themBudget overruns have an accountable personClear escalation when something goes wrong
Run by
Shared Responsibility RegisterGovernance Assessment wizard
Data
Questionnaire
Evidence produced
Ownership register: system, business owner, technical owner, risk acceptor, budget holder, escalation path.
Maps to
NIST AI RMF: Govern; ISO/IEC 42001: roles and responsibilities
AICost-GovControl104Partly automated
Model registry and lifecycle
What it is

A register of the models in use, their versions, where they run, their price, and the triggers to review or retire them.

Why it is needed

Models change under you: new versions, price changes, deprecations. Each change can move quality, risk and cost.

Why enterprises need it

Teams need to know which workloads break or get more expensive when a vendor retires or reprices a model.

What you get:No surprise retirementsPrice changes caught before the invoiceReassessment triggered by real change
Run by
Model and Vendor Decision engineModel Compare enginePrice Watch engineModel Upkeep engine
Data
AI gateway telemetry, Questionnaire
Evidence produced
Model register: model, version, provider, workloads using it, price per million tokens, status, next review, retirement date.
Maps to
NIST AI RMF: Manage; ISO/IEC 42001: AI system life cycle

Frameworks

NIST AI RMF and NIST AI 600-1Govern, Map, Measure and Manage activities, including the generative AI risks
ISO/IEC 42001AI management system: policy, roles, risk treatment, suppliers, monitoring, internal audit, improvement

Stakeholders

Business unit leadsCIOAI platform owner
← 1. AI inventory and shadow AI All modules 3. Use-case value and priorities →
📖 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 →