GitHub Copilot's New Pricing Model: What Happens When You Switch from Monthly Subscriptions to Pay-as-You-Go

Starting June 1, 2026, GitHub will change how it charges for Copilot. Instead of a flat monthly fee, you'll pay based on how much you actually use the AI tool—think of it like switching from a fixed phone plan to one where you only pay for the data you consume.

The new system measures what you use in three categories: input tokens (the code and context you send to the AI), output tokens (the suggestions the AI sends back), and cached tokens (results the system already computed and is reusing from storage). Each type of token gets converted to AI credits, with different rates depending on which AI model is handling your request. The basic unit is simple: one AI credit costs $0.01.

How Individual Plans Will Work

GitHub is offering a broader range of subscription tiers now. Beyond the familiar Copilot Pro, there's Copilot Free, Copilot Pro+, and Copilot Max. Each tier comes with a monthly allocation of credits, though GitHub hasn't yet published exactly how many credits each one includes.

If you use GitHub's mobile app on iOS or Android, you won't be able to buy extra credits beyond what comes with your subscription. This is a standard limitation on mobile platforms, where app stores (Apple and Google) require payments to flow through their systems rather than directly to the developer.

How Teams and Companies Will Handle It

Organizations and enterprises can now pool their credits across all users. Instead of each developer having a fixed monthly cost, the company gets a shared credit budget. Heavy users draw from the pool, while developers who use Copilot less often leave credits unused—which can then be distributed elsewhere in the organization.

This is different from the old model, where each developer's license cost the same regardless of whether they used Copilot constantly or rarely. With a shared credit pool, companies can adjust their spending based on actual usage patterns rather than guessing upfront.

Why Credits Measure Things This Way

The token system exists because not all AI requests cost the same to run. A simple autocomplete suggestion takes less computer power than a complex refactoring that rewrites multiple files. By measuring input, output, and cached tokens separately, GitHub can charge you fairly based on what the AI actually does on your behalf.

Cached tokens are worth noting: when the system can reuse a result it already computed, it charges you less than it would for running the AI fresh. This creates an incentive for GitHub to invest in caching infrastructure—it saves the company money, and those savings can be reflected in what you pay.

This approach aligns GitHub's costs with the underlying economics of running large language models. The bigger and more complex your request, the more tokens it consumes, and the more you pay. It's more transparent than a flat fee, where a developer grinding through a refactoring pays the same as one making a few simple completions.

What This Reflects About the AI Tools Market

We've seen similar shifts before. When cloud platforms like AWS matured, they moved from charging per server to charging for actual computing time and storage used. Google Cloud switched to per-second billing for compute resources. The pattern is consistent: as a technology matures, pricing evolves to match actual costs rather than broad access tiers.

By the time Copilot reaches its fifth year of availability later in 2026, GitHub will have years of data on how different customers use the tool. The company has probably noticed that some teams use it lightly while others rely on it constantly. Usage-based billing lets GitHub charge each customer fairly based on what they actually do, rather than subsidizing heavy users through flat fees or overcharging lighter ones.

This change also signals something broader: AI development tools are moving from experimental add-ons to core infrastructure that companies depend on. As that happens, pricing naturally becomes more sophisticated and consumption-based, like the enterprise software that came before it.

For teams starting to budget for Copilot, the main shift is that you'll need to monitor how your developers use it. Organizations may discover that certain workflows or tasks consume more credits than others, which could influence how teams integrate the tool into their daily work. It's not that this is good or bad—it's just a new dimension of visibility into what using AI-assisted coding actually costs.