How often is the pricing data updated?

The catalog refreshes hourly. Each row in the table shows the exact "last verified" timestamp alongside a link to the provider source it was read from, so you can confirm a price yourself rather than trusting a stale snapshot.

Do I need an account to compare prices?

No. Browsing, filtering, reading the chart, copying share links, and using the ROI calculator are all account-free.

What does "quality tier" mean on the chart and table?

It's a rough capability class — frontier, balanced, or budget — used to group models with similar strengths, not a numeric benchmark score. Use it to avoid comparing a narrow classifier against a frontier reasoning model as if they were substitutes.

Why do some models show no current price?

When a provider changes a rate by more than a review threshold, the new price is held for manual confirmation instead of being published automatically, so a model can briefly show no current price rather than an unverified one.

Is the price shown here what I'll actually be billed?

Treat it as an informational estimate, not an invoice. Provider pricing pages are the source of truth — this table exists to help you compare quickly, not to replace checking directly before you commit spend.

ImplementationAll guides

AI tool rollout and governance checklist

Last updated 2026-07-18

Abstract hand-drawn AI governance control panel with shields keys and approval gates — Original AICostCompass illustration. Abstract generated artwork; no people or animals.

AI adoption tends to spread sideways. One team tests a coding assistant, another uploads documents to a chat tool, and a third starts using an API for support routing. That is how useful technology often enters a company, but it also creates risk if nobody owns data boundaries, spend, quality standards, or vendor settings. Governance should make good use easier, not turn every experiment into a committee meeting.

A practical governance loop

Step 1

Define approved use cases, data boundaries, and review requirements before rollout.

Step 2

Assign owners for seats, API keys, spend limits, vendor settings, and usage reporting.

Step 3

Review adoption, quality, cost, and incidents on a schedule, then update the policy.

Start with allowed use cases

A short list of approved use cases is more useful than a long policy nobody reads. Define what teams may do, what data they may use, and when human review is required. For example: internal summarization may be allowed, customer-facing replies may require approval, and sensitive personal data may be restricted to approved enterprise tools.

Control data access and retention

Review whether prompts, files, code, chat history, customer records, or outputs are stored, retained, or used for training. Business and enterprise plans often include stronger admin controls than consumer plans, but the details vary by vendor. Treat data settings as part of the buying decision, not an afterthought.

Own the budget

Someone should be responsible for seat assignment, inactive users, API keys, usage alerts, and monthly review. Without ownership, teams can accumulate unused seats or let successful API workflows grow past the original budget. Spend controls are not just a finance concern; they are what let teams experiment without surprise bills.

Set a human quality standard

Teams need to know when AI output can be used directly, when it is a draft, and when it needs formal review. The answer will differ by workflow. A meeting summary has a different risk profile than a legal clause, medical note, security patch, or customer refund decision.

References and fact checks

OpenAI business pricing and controls - lists workspace administration, security, privacy, analytics, and enterprise controls
GitHub Copilot organization billing - covers seat assignment, billing cycles, managing costs, and organizational controls
Cursor pricing and team controls - documents privacy mode enforcement, admin dashboard, SSO, spending limits, and active seats

How to do this in AICC

Turn the article into an answer you can use

Use AICC to turn rollout governance into a visible operating plan: what tools are approved, what they cost at team scale, where review is required, and which usage needs budget controls.

AICC workflow map

Move 1

Define allowed use cases

Move 2

Price team-scale usage

Move 3

Set review and spend controls

1
List the teams and workflows
Start on the Guides hub and write down the teams, data types, and workflows that are in scope. Separate low-risk internal drafting from customer-facing, regulated, or production-impacting work.
2
Use Pricing to identify manageable tool classes
Open the Pricing map and filter for the tool categories you expect to approve. Look for pricing units, source freshness, model tier, and whether the tool is seat-based, token-based, or media-based.
3
Use Compare for rollout volume
Open the Comparison tool and enter rollout assumptions, not pilot assumptions. Use expected seats, monthly tokens, images, video seconds, audio characters, or other relevant units so the governance plan has a real budget.
4
Use ROI to justify controls
Open the ROI calculator and model saved time after review. If the ROI depends on sensitive data or high-risk output, make human review part of the operating standard rather than an afterthought.

What you should have at the end

You should leave with an approval shortlist, a realistic monthly budget, and a simple policy: who may use which tools, with what data, under what review rules, and who owns the spend.

Guides hub

Use the supporting articles when you need to refine assumptions or explain the decision.

Pricing map

Scan providers, model tiers, unit prices, quality signals, and visible market options.

Comparison tool

Enter your workload assumptions and compare estimated monthly cost across selected tools.

ROI calculator

Translate AI spend into saved time, labor value, and business return.

Frequently asked questions

Why does AI adoption often spread through a company without anyone noticing?

Individual teams tend to test tools independently — one on a coding assistant, another uploading documents to a chat tool — which is useful but creates risk if nobody owns data boundaries or spend.

What four questions should a lightweight AI governance policy answer?

What can we use, what data can it touch, who checks the output, and who owns the cost.

Should governance rules be the same for every AI use case?

No. Risk-appropriate review matters more than a blanket policy — internal summarization may need little oversight, while customer-facing or regulated workflows should require approval.