GitHub Copilot token-based billing: what changes for your company

Updated October 4, 2026

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

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

It depends on your team's usage intensity. For those who use Copilot lightly, the cost remains manageable; for teams working on long agentic sessions, the bill can multiply in a few weeks.

GitHub Copilot is the most popular AI (artificial intelligence) assisted writing tool among developers. However, from June 1, 2026, the rules of the game have changed: the fixed monthly price has been replaced by a pay-as-you-go system based on AI Credits — digital credits that run out as you use the tool's advanced features.

The question that IT managers and Italian SME owners are asking themselves today is simple: is it still worth keeping, or is it better to look elsewhere?

What exactly changed from June 1, 2026?

GitHub has replaced the old Premium Request system with AI Credits: each plan includes a monthly credit equal to the subscription cost, and each advanced request deducts from that credit. Basic code completions remain free.

The GitHub's official blog explained the logic: 1 AI Credit equals $0.01. A $10/month Pro plan therefore includes 1,000 AI Credits. Once you run out, each additional request is charged separately.

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

GitHub justified the choice by stating that the old system was no longer sustainable: a brief chat query and an autonomous coding session of several hours cost the user the same amount, while GitHub absorbed increasing inference costs. This is reported by The Decoder .

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

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

The most cited case in the community is the one documented by Visual Studio Magazine : a developer on a $10/month Pro plan conducted a single troubleshooting session (solving a complex technical problem) and found themselves with a projected expense of $180 for June alone.

This is not an isolated case. Heavy users — those who use Copilot for many hours a day on complex projects — report bills rising from $39 to over $800 monthly. The community's reaction was immediate and very harsh.

The announcement generated over 400 comments and almost 900 downvotes in the official GitHub community thread. TechCrunch collected the harshest reactions: some developers called the move "a joke" , others openly talk about the end of Copilot's golden era. The news was also reported 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 control tools turns a stable budget item into a hard-to-manage financial risk.

The flat-rate model — fixed monthly price per user — had a huge advantage: certainty. You knew exactly how much you were spending each month, regardless of how many requests you made. With the new system, each intense session can generate a cost that you have no way of predicting in advance.

The second issue is transparency. Consumption monitoring tools were initially considered inadequate. Microsoft responded by releasing VS Code 1.125 with a spend meter integrated — a real-time visible spending counter in the editor's status bar — but many users consider it a late patch on an already open wound. This is reported by Visual Studio Magazine .

There's also a matter of trust. Many companies had built their annual IT budgets on a fixed cost for Copilot. Retroactively changing the rules creates tension, especially when there was no gradual transition period.

What is happening to market shares of AI tools for developers?

Copilot's workplace adoption dropped from 29% to 21% between January and July 2026, while Claude Code rose from 18% to 39% in the same period, according to 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 dollars in 2026, up from 7.37 billion in 2025, with a 74% year-over-year growth, according to AI Business Weekly . The pie is getting bigger, but the slices are being redistributed.

What alternatives exist and how do they compare to Copilot?

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

Before deciding whether to stick with Copilot or migrate, it's worth doing a structured comparison. The issue of AI cost governance affects all tools in the industry — not just Copilot — as we also analyzed when discussing Microsoft Copilot Autopilot and variable pricing .

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

For an Italian SME, switching to a variable cost requires three things: figuring out how much you consume today, setting internal usage rules, and evaluating if your current budget can handle the new model.

Let us think about three typical scenarios for Italian companies. The numbers are just examples: they show the scale of the issue, not real customer data.

What rules and risks need to be managed immediately?

The main risk isn't the cost itself, but the lack of control. Without internal policies and monitoring tools, a single intense session can generate expenses that no one has authorized.

The issue of AI tool governance isn't just about Copilot. It's the same problem we analyzed when talking about AI agents and governance : when a tool acts autonomously, costs can grow before anyone notices in time.

For companies using Copilot in enterprise or team plans, it is necessary to involve financial managers in policy review from the outset. A variable cost requires different control mechanisms than a fixed license.

Why is this change happening right now?

Latest-generation AI models cost much more to run than previous versions. GitHub is passing the unpredictability of these infrastructure costs onto the end user.

Large language models with extended context windows—meaning they can analyze entire codebases in a single session—consume significantly more computational resources than their predecessors. GitHub 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 such rapid growth, providers are progressively abandoning flat-rate models to embrace pay-as-you-go pricing logic. This isn't an isolated GitHub phenomenon: the same pattern is seen in other enterprise AI tools, as we documented when analyzing Google's AI licenses to publishers .

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

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

Before making any decision, you need to understand how much your team actually consumes 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 frequent mistake is doing nothing while waiting for GitHub to fix the model. Meanwhile, the bills keep coming.

• Wait without monitoring. Many teams are waiting for GitHub to introduce monthly caps or tiers with included tokens. It is likely to happen, but in the meantime, consumption does not stop.
• Treat AI cost as a fixed expense. The new model calls for an approach similar to performance marketing campaigns: flexible budgets, continuous tracking, and constant tweaking.
• Don't involve the CFO. A variable cost that can multiply by 10 isn't just a technical decision. It needs to be handled with finance leads from day one.
• Migrate quickly without evaluating. Switching to an alternative without a structured test can create integration and productivity problems worse than the ones you want to solve.
• Ignore features that don't consume credits. Code completions stay free. For many teams, they already make up 70-80% of Copilot's value—worth checking if that's enough.

How to know if the situation is under control?

Three indicators are enough to understand if you are managing the new model well: the monthly cost per developer, the percentage of advanced requests on the total, and the variance between months.

So, is it still worth it for your company?

It is worth it if your team uses Copilot mainly for code completion and chat sessions are short and focused. In that case, the credit included in the plan covers most of the usage and the cost remains predictable.

A middle ground you have a mixed team: some developers use advanced features intensively, others don't. In that case, it's worth segmenting plans by usage profile and actively monitoring spending for at least two months before deciding.

Not yet if your team works on long agentic sessions or complex codebase analyses every day. The risk of unexpected bills is high, and alternatives like Cursor or Claude Code are worth serious consideration before renewing your contract.

How we see it at SHM

For us, GitHub Copilot's shift to token-based billing is a clear signal: AI is no longer a fixed cost to be forgotten in the annual budget. It's a tool that requires active governance, just like an advertising campaign or a pay-as-you-go supply contract.

We believe AI should be integrated into business processes to free teams from routine tasks—not to create new sources of financial anxiety. When a tool generates 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 AI tools we are seeing evolve rapidly, from new Microsoft Copilot models for marketing to autonomous agents. Cost governance is the new battleground to manage.

If you are evaluating how to structure the adoption of AI tools in your team in a sustainable way, our service of strategic AI integration is the starting point. For those who also want to train their team on these topics, the service of AI training for businesses offers practical paths designed for Italian SMEs. You can also explore our hub on AI and process automation for all 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 are spending on AI, and this is a risk that needs to be managed before the next bill.