AI governance for engineering teams means four things in one place: seeing who spends what, setting hard limits, controlling which models people can use, and catching abuse. Get those four right and your team keeps the speed of AI coding while you keep the bill predictable. This page walks through each one, and how Clawgate does all of it from a single dashboard.
Most teams start with only one of the four. They have a monthly invoice but no idea who ran it up, or they have a spend alert that arrives after the money is already gone. Real spend control is not a report you read on the first of the month. It is a set of rules that run on every request, before the model is ever called.
The three blind spots that make AI spend a mystery
When AI coding costs surprise a team, it is almost always because of three gaps at once.
- No usage control. There is no cap. A developer can run the most expensive model on the largest context all day, and nothing stops it until the invoice arrives.
- No cost visibility. You know the total, but not who spent it, on which project, or whether an expensive model was even needed. Enterprise AI cost management falls apart when the numbers are one undated lump.
- No model control. Every key can reach every model, so the priciest model gets used for jobs a far cheaper one would have handled just as well.
These are not hypothetical. Uber used its entire 2026 AI budget in about four months after giving Claude Code to roughly 5,000 engineers, with cost per engineer reaching $500 to $2,000 a month, and it responded with blunt usage caps (Fortune). Meta staff burned 73.7 trillion tokens in 30 days racing an internal leaderboard, and the company moved to cap employee AI usage (The Information). Even at a normal pace, Anthropic's own docs put Claude Code at roughly $13 per developer per active day, about $150 to $250 per developer per month, with heavy users higher. Multiply that across a team and the three blind spots turn into a real number fast.
The governance features that close the gaps
Here is what a control layer needs to give an engineering team, and how each piece works in Clawgate.
Hard-stop budgets
Budgets are the foundation of any FinOps for AI setup, and they only work if they actually stop spend. In Clawgate, budgets are hard stops: daily and weekly caps in dollars, tokens, or sessions, set per user and per project. When a limit is hit, the request is blocked. It returns a 403 so the developer sees the limit message, instead of a 429 that coding agents silently retry. Project budgets are an all-time spend cap checked before the per-user quotas, so a project can never quietly overrun. You set all of this from a dashboard, not a config file.
Per-user and per-project cost tracking
Every key is tied to a person and a project, so per-user and per-project cost is tracked automatically, live and historical. That is your team AI usage monitoring and your AI usage analytics dashboard in one view: no manual tagging, no reconciling receipts. Because attribution is built in, AI cost allocation and chargeback AI costs by team become simple. You can bill client work back to the right client, or split the internal AI bill across teams by project. For a deeper walkthrough of the reporting, see how to track Claude Code usage by developer and project.
Model control
A policy limits which models a key can use, and you can force a specific model for a project. You switch models from the dashboard without touching anyone's local setup. This is one of the biggest levers in enterprise AI cost management, because the same task can cost about 12 times more on one model than another. In one experiment across six models on the same prompt, the cheapest run came in at $0.2576 and the priciest at $3.1230. Routing routine work to a cheaper capable model often saves more than any cap.
Abuse and impersonation detection
Budgets stop honest overspend. Abuse detection stops the dishonest kind. Content-fingerprint detection catches leaked or shared keys and impersonation, where a request's content does not match the project it claims. So if a key leaks or someone tries to bill their work to another team's project, you find out instead of paying for it silently.
Threshold alerts
You do not want to learn about a problem only when the hard cap slams shut. Configurable alerts email you when spend crosses a threshold you set, 80% by default, so you can top up, switch to a cheaper model, or pause before the cap is reached. The hard stop is the safety net; the alert is the early warning.
Audit-friendly usage records
Every request produces a usage record tied to a person, a project, and a model, kept live and historical. That gives you the paper trail a finance or security review will ask for, without anyone exporting logs by hand.
Why we built all three into one tool
When we looked at what was available, no single existing tool did all three of the things a team actually needs, so we built Clawgate to do them together. A team needs to monitor and control spend, it needs to cut token usage, and it needs to catch abuse and impersonation. Most tools do one of those well and ignore the other two.
- Monitor and control. Hard-stop budgets, per-user and per-project cost attribution, and model control, all covered above.
- Cut token usage. Opt-in prompt compression cuts up to 92% of the tokens agents resend each turn on heavy workloads, while accuracy holds. Old repeated context is compressed; the recent turns, system prompt, tools, and thinking blocks are left untouched, and the savings show up in dollars on the dashboard. You can also route work to cheaper capable models. The AI providers will not build this, because tokens are their revenue. They offer prompt caching, but nothing that actively compresses context or routes you to a cheaper model.
- Catch abuse and impersonation. Content-fingerprint abuse detection flags leaked or shared keys and impersonation.
This is not a theory we sketched on a whiteboard. Clawgate started as an internal tool at Virstack that cut our own team's AI bill by 70% before it became a product. We needed all three at once, so we built all three, and that firsthand experience is why the governance model is shaped the way it is.
Fair pricing, so control does not mean surprise bills
Governance should not come with a pricing gotcha. Clawgate bills AI at provider cost plus one clear platform fee: free to start, then 8% on Pay As You Go, $5 per seat plus 3% on Team, and $10 per seat plus 2% on Business. Enterprise can self-host in their own infrastructure with no per-token markup. Because budgets are hard stops, there are no surprise bills: the number you set is the ceiling. The full breakdown is on the pricing page.
If you want the narrower how-to versions of this, we cover how to control Claude Code costs and the best Claude Code cost management tools compared in more detail.
Frequently asked questions
How do I control AI spend across a whole team?
Put a gateway between your developers and the AI providers. Every developer points Claude Code or Codex at the gateway with one key, and every request is authenticated, tied to a person and a project, and checked against a budget before it reaches the model. With Clawgate you set hard-stop budgets per user and per project in dollars, tokens, or sessions. When a limit is hit the request is blocked, so the spend can never run past the cap you set.
Can I allocate AI costs to different teams or projects?
Yes. Every key in Clawgate is tied to a person and a project, so per-user and per-project cost is tracked automatically, live and historical, with no manual tagging. That makes AI cost allocation and chargeback straightforward: you can bill client work back to the right client, or split the AI bill across internal teams by project.
How do I stop misuse of company AI?
Two layers. Hard-stop budgets cap what any key can spend, so a leaked or shared key cannot drain your account. On top of that, content-fingerprint abuse detection flags leaked or shared keys and impersonation, where a request's content does not match the project it claims. Threshold alerts email you when spend crosses a level you set (default 80%) so you can act before the cap is reached.
Does this work for both Claude Code and Codex?
Yes. Clawgate is a control layer for Claude Code and OpenAI Codex, and also supports opencode and Cursor. Developers point whichever tool they use at the gateway with one key, so budgets, model control, cost attribution, and abuse detection cover both Claude Code and Codex from a single dashboard and a single bill.
Written by Chathuranga K, an engineer at Virstack.
Give your team AI, keep control of the spend
Connect Claude Code and Codex once, set hard-stop budgets per developer and per project, see who spent what, and catch abuse before it hits the bill.