Imagine this story of two developers

Rahul is a developer working on a normal Monday morning.
He opens VS Code, asks GitHub Copilot to generate a small API function, accepts a few code suggestions, and moves on.

Meanwhile, Priya on another team is doing something completely different.

She asks Copilot to:

• analyze an entire repository,
• generate architecture suggestions,
• run multi-step agent workflows,
• review pull requests,
• and iterate through multiple files for nearly an hour.

Under the old billing model…

both Rahul and Priya could end up consuming almost the same “premium requests.”

That’s where the problem started.

GitHub Realized Something Important

GitHub Copilot is no longer just an autocomplete tool.

It has evolved into something much bigger:

• an AI coding assistant,
• a repository analyst,
• a pull request reviewer,
• and increasingly… an autonomous coding agent.

GitHub itself described Copilot as becoming an “agentic platform capable of running long, multi-step coding sessions.”

So GitHub introduced something new:

Usage-Based Billing

Starting June 1, 2026, GitHub Copilot is moving from request-based billing to a system powered by GitHub AI Credits.

Once you understand the reason behind it, the change becomes much easier to understand.

First, Let’s Understand the Old Model Premium Request Units (PRUs)

Think of PRUs like movie tickets.

Whether you watched:

• a short 5-minute clip,
  or
• a 3-hour blockbuster,

you still used “one ticket.”

That’s how Copilot billing started feeling.

A small AI interaction and a massive AI-heavy task were often treated similarly.

But AI doesn’t work like that internally.

Some tasks consume:

• far more GPU power,
• more memory,
• more tokens,
• and significantly more compute cost.

GitHub eventually reached a point where the old system became difficult to sustain.

So What Exactly Is Usage-Based Billing?

Now imagine electricity billing.

You pay based on:

• how much electricity you consume,
  not
• how many times you switched on the light.

That’s essentially what GitHub is doing.

Instead of counting requests…

GitHub will measure actual AI consumption using:

• input tokens,
• output tokens,
• cached tokens,
• and the AI model being used.

This consumption is converted into:

GitHub AI Credits

And GitHub defines it very clearly: 1 AI Credit = $0.01 USD

The Best Way to Understand It

Here’s a real-world style comparison.

Activity	AI Consumption
Small code completion	Very low
Quick chat question	Low
Generating unit tests for one file	Medium
Repository-wide analysis	High
Long agentic coding session	Very High

So now…

users consuming more AI compute will naturally consume more AI credits.

What Organizations & Enterprises Need to Know

This is where the official GitHub documentation becomes very important.

For organizations and enterprises, GitHub is introducing pooled AI credits.

And this is actually a smart move.

Let’s say a company has:

• 100 Copilot Business users.

Earlier:
each user effectively had isolated usage.

Now:
their credits are pooled together.

GitHub gave this exact concept in documentation:

A company with 100 Copilot Business users gets:

• one shared pool of AI credits,
  instead of
• 100 isolated buckets.

That means:

• lighter users naturally balance heavier users,
• unused credits are not wasted,
• and organizations get better resource flexibility.

Included AI Credits Per Plan

GitHub also clarified how many credits organizations receive monthly.

Plan	Included AI Credits Per User / Month
Copilot Business	1,900 AI Credits
Copilot Enterprise	3,900 AI Credits

And for existing customers, GitHub is temporarily increasing those limits during the transition period (June–September 2026):

Plan	Promotional Credits
Business	3,000
Enterprise	7,000

This is GitHub’s way of helping organizations adapt before normal billing begins.

The Hidden Enterprise Feature Nobody Talks About

Budget Controls.

And honestly, this may become one of the most important features for enterprises.

GitHub is introducing:

• enterprise-level budgets,
• organization-level budgets,
• cost-center budgets,
• and even user-level budgets.

Imagine a scenario:

One enthusiastic developer accidentally runs huge AI workflows daily.

Without controls?

Costs could spike fast.

Now admins can:

• track usage,
• set limits,
• receive alerts,
• and even stop usage automatically when budgets are exhausted.

Final Thought

GitHub Copilot is quietly moving from:

“AI assistant”

to

“AI infrastructure.”

And infrastructure always eventually becomes usage-based.

This shift isn’t just about pricing.

It signals something bigger:

AI coding tools are no longer lightweight helpers sitting beside developers.

They are becoming powerful compute systems capable of reasoning across entire codebases.

And once that happens…

billing starts looking less like a software subscription, and more like cloud consumption.