GitHub Copilot Token-Based Billing: What Changes for Your Company

Updated October 4, 2026

A $10-a-month plan turning into a $180 bill on the first day of June: it's not a mistake, it's GitHub Copilot's new model. If your team uses this tool, you need to understand what has changed, and fast.

Is GitHub Copilot still worth it, or is it time to switch?

It depends on how intensively your team uses it. For light Copilot users, the cost stays manageable; for teams running long agentic sessions, the bill can multiply within a few weeks.

GitHub Copilot is the most widely used AI (artificial intelligence) coding assistant among developers. Since June 1, 2026, however, the rules of the game have changed: the fixed monthly price has been replaced by a usage-based system built on AI Credits, digital credits that run down as you use the tool's advanced features.

The question IT managers and Italian SME owners are asking today is simple: is it still worth keeping, or should you look elsewhere?

What exactly changed on June 1, 2026?

GitHub has replaced the old Premium Request system with AI Credits: every plan includes a monthly credit equal to the subscription price, and every advanced request draws down that credit. Basic code completions remain free.

The official GitHub blog explained the logic: 1 AI Credit equals $0.01. A Pro plan at $10 a month therefore includes 1,000 AI Credits. Once you use them up, each additional request is billed separately.

The features excluded from the count, and therefore still free, are code completions (the automatic suggestions as you type) and Next Edit Suggestions (contextual edit proposals). Everything else, including chat, agentic sessions and codebase analysis, consumes credits.

GitHub justified the move by saying the old system was no longer sustainable: a quick chat question and a multi-hour autonomous coding session cost the user the same amount, while GitHub absorbed rising inference costs. This is reported by The Decoder.

How much did the first bill really cost those who already switched plans?

Documented cases point to extreme increases: from $39 to over $800 a month for heavy users, and a projection of $180 on day one for a $10 plan.

The case cited most often in the community was documented by Visual Studio Magazine: a developer on the $10-a-month Pro plan ran a single troubleshooting session (solving a complex technical problem) and ended up with a projected bill of $180 for June alone.

It's not an isolated case. Heavy users, meaning people who use Copilot many hours a day on complex projects, report bills jumping from $39 to over $800 a month. The community's reaction was immediate and harsh.

The announcement drew over 400 comments and nearly 900 downvotes in the official GitHub community thread. TechCrunch gathered the harshest reactions: some developers called the move "a joke", while others openly talk about the end of Copilot's golden age. The story was also covered by TechCrunch.

Why is the community reacting so harshly?

The problem isn't just the cost. It's the unpredictability. A pay-as-you-go model without clear controls turns a stable budget line into a financial risk that's hard to manage.

The flat-rate model, a fixed monthly price per user, had one huge advantage: certainty. You knew exactly what you'd spend each month, no matter how many requests you made. With the new system, every intensive session can generate a cost you have no way of predicting in advance.

The second problem is transparency. Usage monitoring tools were initially seen as inadequate. Microsoft responded by releasing VS Code 1.125 with a built-in spend meter, a spending counter visible in real time in the editor's status bar. But many users see it as a late patch on a wound that's already open. Visual Studio Magazine reports on it.

Then there's the matter of trust. Many companies had built their annual IT budgets around a fixed line for Copilot. Changing the rules retroactively creates tension, especially when there was no gradual transition period.

What is happening to market share among AI tools for developers?

Workplace adoption of Copilot fell from 29% to 21% between January and July 2026, while Claude Code rose from 18% to 39% over the same period, according to the JetBrains survey of over 15,000 developers.

The JetBrains Developer Ecosystem survey (May–July 2026, over 15,000 professional developers) shows a significant shift. The data is reported by Quash.

The AI coding tool market as a whole keeps growing: it reached $12.8 billion in 2026, up from $7.37 billion in 2025, a 74% year-over-year increase, according to AI Business Weekly. The pie is getting bigger, but the slices are being redistributed.

What alternatives exist and how do they compare with Copilot?

The AI coding assistant market is far more competitive today than it was two years ago. Cursor, Claude Code and JetBrains' native AI features deserve a fresh look.

Before deciding whether to stay on Copilot or switch, it's worth doing a structured comparison. The challenge of governing AI costs affects every tool in the industry, not just Copilot, as we also discussed in Microsoft Copilot Autopilot and variable pricing.

What does all this mean for an Italian SME with a development team?

For an Italian SME, moving to a variable cost takes three things: understanding how much you use today, setting internal usage rules, and checking whether your current budget can handle the new model.

Let's look at three typical scenarios for Italian companies. The numbers are illustrative: they show the order of magnitude of the problem and are not real client data.

Which rules and risks do you need to handle right away?

The main risk isn't the cost itself but the lack of control. Without internal policies and monitoring tools, a single intensive session can generate spending that nobody authorized.

AI tool governance isn't just a Copilot issue. It's the same problem we covered when talking about AI agents and governance: when a tool acts autonomously, costs can climb without anyone noticing in time.

If your company uses Copilot on enterprise or team plans, bring your finance leads into the policy review right away. A variable cost calls for different control mechanisms than a fixed license.

Why is this change happening right now?

The latest-generation AI models cost far more to run than earlier versions. GitHub is passing the variability of these infrastructure costs on to the end user.

Language models with extended context windows, meaning those that can analyze entire codebases in a single session, consume far more computing resources than earlier versions. GitHub has explicitly stated that the old system was no longer sustainable.

The AI coding tool market reached $12.8 billion in 2026, according to AI Business Weekly. With growth this fast, providers are gradually moving away from flat-rate models and embracing usage-based pricing. It's not just a GitHub thing: the same pattern shows up in other enterprise AI tools, as we documented when looking at Google's AI licenses for publishers.

According to SQ Magazine, Copilot reached 4.7 million paying subscribers in January 2026, up 75% year over year. At that scale, even a small change in per-user inference costs becomes a huge problem for Microsoft.

How do you get started: what to do right now, step by step?

Before making any decision, you need to understand how much your team really uses today. Only with that data can you estimate the real impact of the new model.

What are the most common mistakes to avoid?

The most common mistake is doing nothing and waiting for GitHub to fix the model. Meanwhile, the bills keep coming.

• Waiting without monitoring. Many teams are waiting for GitHub to introduce monthly caps or tiers with included tokens. It will probably happen, but in the meantime usage doesn't stop.
• Treating AI cost as a fixed line item. The new model calls for an approach similar to performance advertising campaigns: flexible budget, continuous monitoring, constant optimization.
• Not involving the CFO. A variable cost that can multiply by 10 isn't just a technical decision. It needs to be managed with finance leaders from day one.
• Migrating in a rush without evaluating. Switching to an alternative without a structured test can create integration and productivity problems worse than the ones you're trying to solve.
• Ignoring features that don't use credits. Code completions remain free. For many teams, they already account for 70-80% of Copilot's value: it's worth finding out whether they're enough.

How can you tell if the situation is under control?

Three indicators are enough to tell whether you're managing the new model well: monthly cost per developer, the share of advanced requests out of the total, and month-to-month variance.

So, is it still worth it for your company?

It's worth it if your team uses Copilot mainly for code completions and chat sessions are short and targeted. In that case, the credit included in the plan covers most of the usage and the cost stays predictable.

Somewhere in between: you have a mixed team, with some developers using advanced features heavily and others not. In that case, it's worth segmenting plans by usage profile and actively monitoring spend for at least two months before deciding.

Not yet if your team works on long agentic sessions or complex codebase analysis every day. The risk of unexpected bills is high, and alternatives like Cursor or Claude Code deserve serious evaluation before you renew the contract.

How we see it at SHM

For us, GitHub Copilot's move to token-based billing is a clear signal: AI is no longer a fixed cost to set and forget in the annual budget. It's a tool that requires active governance, just like an advertising campaign or a usage-based supply contract.

We believe AI should be integrated into business processes to free teams from routine, not to create new sources of financial anxiety. When a tool creates uncertainty instead of value, the problem isn't the tool itself: it's the lack of a structured adoption strategy.

The same principle applies to all the AI tools we're watching evolve at speed, from new Microsoft Copilot models for marketing to autonomous agents. Cost governance is the new ground to cover.

If you're thinking about how to structure AI tool adoption in your team in a sustainable way, our strategic AI integration service is the place to start. If you also want to train your team on these topics, our AI training for companies offers practical programs designed for Italian SMEs. You can also explore our hub on AI and process automation for all the latest industry updates.

Frequently Asked Questions

The real problem isn't how much Copilot costs: it's that many companies still don't know how much they're spending on AI, and that's a risk to manage before the next invoice.