AI features are everywhere.
Agents. Assistants. Token-based APIs. Outcome-based pricing.
Every product team is racing to ship AI. Every company is racing to figure out how to sell it.
For a while, AI monetization felt straightforward. Measure usage, charge for it, send an invoice.
But the moment you sell AI into the enterprise, a completely new problem shows up. One that no existing billing, metering, or pricing tool was built to solve.
Selling AI usage is easy.
Controlling committed AI usage in real time is not.
And when you cannot control it, usage stops being an asset and becomes a liability. Surprise overages. Internal escalations. Frustrated customers. Revenue that is hard to forecast.
AI monetization breaks when usage is real-time, but controls are not
Most teams hit this wall faster than they expect.
A single user burns through nearly an entire company’s AI credits in a day.
One team accidentally drains the organization’s monthly token budget.
A new customer commits to usage across multiple products and expects clear limits and enforcement from day one.
AI usage does not behave like traditional SaaS.
It is dynamic, spiky, and increasingly driven by agents acting autonomously across workflows rather than by humans clicking buttons.
Yet until now, there has been no way to govern AI usage as it actually happens.
Existing tools can report usage after the fact.
They cannot control it at runtime.
That gap is where AI monetization breaks. And your largest customers feel it immediately.
They do not just ask about pricing. They ask who gets alerted when limits are approaching, what happens when a limit is reached, whether budgets can be allocated by team or region, and whether runaway usage can be stopped before it becomes a problem.
Enterprise customers do not just want to pay for AI usage.
They want control. They expect governance. And they expect it inside the product.
Do not wait for a support ticket to learn you are out of budget
At Stigg, we have helped teams launch flexible monetization models without rebuilding their billing stack.
AI introduced a new requirement. Usage is not something you reconcile later. It is something you must manage at runtime.
That is why we built AI Usage Management. The first real-time governance layer designed specifically for AI-native products.
This is not another dashboard. It is not a billing add-on. And it is not something teams have quietly been doing already.
Until now, there has been no product that could enforce AI usage commitments in real time across agents, teams, and workflows.
AI Usage Management is now available in Early Access.
It works across any usage model, any agent behavior, and any enforcement policy you need, giving you real control over AI spend as it happens.
Give customers the controls they will demand on the first enterprise call
If you are selling into serious accounts, “we will figure it out later” is not acceptable.
Enterprise buyers expect AI spend to be safe, predictable, and governable without routing every exception through your team.
AI Usage Management delivers those controls directly inside your product. Customers can allocate budgets across teams, regions, or users, see budget drawdowns in real time, get alerted before limits are hit, and have policies enforced automatically when usage approaches or exceeds commitments.
This is governance customers can actually use, not promises your team has to manually keep.
Turn usage spikes into predictable outcomes
AI usage does not grow smoothly.
It surges. It concentrates in unexpected places. It is triggered by agents running in the background. And it can move from “everything is fine” to “we need an exception right now” in a single afternoon.
With real-time governance, you do not just discover the problem after the invoice goes out.
You prevent it.
AI Usage Management tracks usage as it happens and applies controls automatically, so customers stay within their commitments and your team stays out of firefighting mode.
Put governance where it belongs, inside the product
Most teams try to patch AI governance together with dashboards, spreadsheets, and internal tools.
That might work early on. It does not scale. And it does not meet enterprise expectations.
AI Usage Management includes an embedded admin UI that lets customers manage budgets, allocations, and policies themselves without waiting on support or engineering.
It lives alongside the rest of your monetization stack.
No new billing system.
No rewrites.
Just real-time control, built into the product.
Built for how AI actually scales inside enterprises
As customers grow, usage stops being per account.
It becomes per department, per region, per team, and sometimes per user, with buyers needing visibility and accountability across all of it.
AI Usage Management was designed for that reality from day one. High-cardinality allocations and governance that match how enterprise organizations actually operate.
Early Access is now open
We are opening Early Access to AI Usage Management and partnering with teams building the next generation of AI-native products.
If your AI usage is growing faster than your ability to control it, this is exactly what we built it for.
This category did not exist.
Now it does.
Built for how AI actually works.
Powered by Stigg.
