GitHub Copilot users are facing a major billing change starting June 1, 2026. The flat monthly subscription model is ending. Instead, GitHub is switching to a token-based billing system called AI Credits.

Under the old system, developers paid a fixed price per month for access to Copilot’s AI features. That was simple and predictable. Now, costs will depend on how many tokens a user consumes. A token roughly equals part of a word used by the AI models powering Copilot.

This change hits especially hard for power users who run long, complex AI coding sessions. Tasks like Copilot Chat, cloud agents, code reviews, and multi-file refactors will now eat into your monthly AI Credit pool. If you exceed your credits, you pay more, sometimes a lot more.

The upside: basic code completions and quick inline suggestions remain unlimited and free. So if you use Copilot mainly for simple code hints, your bill won’t change much. But if you rely on AI to handle bigger chunks of work, expect your costs to jump.

How Much More Will It Cost?

Many developers have run their numbers and found shockingly high increases. Some who paid around $29 a month could see bills rise to $200 or more. Others reported simulations showing bills climbing from $50 to several thousand dollars if usage stays the same.

GitHub offers different plans with varying AI Credit allowances. For example, the $39 Pro+ plan includes 7,000 AI Credits each month. Once you use those up, you pay $0.01 per extra credit. Heavy agentic workflows can burn through credits fast, especially when using the newest, most powerful models.

GitHub also introduced a Max Plan for very heavy users. It costs more upfront but provides a larger credit pool, which can reduce overage fees. Still, even with this plan, some users see huge cost increases compared to the old flat-rate system.

Why the Change Now?

GitHub’s product leaders say the old pricing model was unsustainable. The AI behind Copilot, especially newer models like GPT-5.5, require massive computing power. GitHub was essentially subsidizing these costs for years, losing close to a billion dollars a year.

With millions of paid subscribers and growing use of agentic AI sessions, the bill for running Copilot’s AI skyrocketed. The token-based system shifts costs back to users based on their actual consumption. This change aligns with the wider AI industry trend of usage-based pricing.

Other AI coding tools from companies like Anthropic and OpenAI already use token billing. GitHub was the last major player with a flat-rate model. Now, everyone is moving toward metered billing to balance costs and usage.

Microsoft’s Internal Shift Reflects the Same Cost Pressures

Inside Microsoft, similar cost concerns have driven changes. The company recently pulled back access to Anthropic’s Claude Code AI for its engineering teams. Instead, Microsoft is pushing its own GitHub Copilot CLI tool.

This move isn’t about model quality—it’s about cost control and tighter integration with Microsoft’s internal systems. Running large-scale AI coding tools across thousands of developers demands careful budget management, security, and workflow control.

Microsoft’s decision highlights what many tech companies face: smart AI models are great, but they must be affordable and manageable at scale. The smartest AI won’t win if it’s too expensive or hard to control internally.

What Developers and Teams Should Do

If you use GitHub Copilot, check your billing dashboard before June 1. GitHub provides tools to simulate your upcoming costs under the new model. This helps avoid unpleasant surprises.

Set spending caps in your GitHub billing settings. You can limit overages so features pause when your credit pool runs out. Normal autocomplete will keep working, so your coding won’t stop completely.

Consider your usage patterns. If you run long AI sessions or complex agent workflows, try switching to lighter models or reduce session lengths. Some developers are offloading heavy tasks to local AI models to avoid high cloud fees.

For teams and enterprises, analyze which users burn the most tokens. Decide who needs higher-tier plans with more credits. Use the transition period to create budgets, spending alerts, and governance policies for AI tool use.

AI coding tools boost productivity, but now they come with a usage cost. The key is to find a balance between AI assistance and managing expenses. Using AI smarter, not just more, will be the winning strategy.