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GitHub has announced that all Copilot plans will move to usage-based billing starting June 1, ending fixed request allowances. Under the new model, Copilot plans will use a credit balance that depletes based on actual use, while base prices remain unchanged: Copilot Pro stays at $10 per month and Copilot Business at $19 per user per month. GitHub said the previous pricing structure was no longer sustainable as infrastructure costs increased.
The move follows a similar pricing change from Anthropic. PYMNTS reported that Anthropic began charging enterprise customers based on AI consumption. Claude Enterprise customers now pay a flat $20 per user per month plus a variable charge tied to computing capacity used. Previously, those customers paid up to $200 per user per month for a fixed usage allotment.
Fredrik Filipsson, co-founder of Redress Compliance, estimated that the change could double or even triple costs for heavy Claude Enterprise users, according to The Information.
With flat subscriptions, developers had less incentive to limit usage, running longer sessions and experimenting more freely. Under usage-based billing, extended sessions carry additional costs, and more capable models run at higher rates.
Reactions to GitHub’s announcement were immediate. Some users argued that the change reduces value even when sticker prices remain the same. GitHub’s FAQ addressed the concern directly, noting that usage-based billing is intended to align costs more closely with actual value and allow developers to choose which models to use.
CNBC reported that Anthropic’s Claude Code surpassed $2.5 billion in annualized revenue by February, up more than 100% since the start of the year. In April, OpenAI introduced a new $100-per-month Codex plan aimed at the same developer audience.
The underlying rationale is consistent across providers: running powerful AI models at scale is costly, and neither company is offering unlimited access indefinitely.
Traditional software costs typically track headcount, while AI costs track activity. A single employee can generate thousands of AI interactions in a day, while another may trigger none. Automated processes can also run continuously without anyone monitoring the bill.
PYMNTS reported that enterprise AI invoices increasingly resemble utility bills rather than software subscriptions, with charges tied to model activity instead of employee count. Finance teams built around stable annual renewals now manage a cost structure without a prior reference point.
Costs can also compound beyond the model subscription itself. According to PYMNTS, for every dollar spent on AI models, businesses spend $5 to $10 on integration, compliance, and monitoring. In that view, the subscription is only the visible line item.
GitHub is introducing administrative controls that allow organizations to cap spending at the company, team, or individual level. Anthropic’s enterprise changes apply to accounts with more than 150 users. Both approaches give procurement teams a way to manage spend, though predicting costs in advance remains a separate challenge.
PYMNTS Intelligence found that more than 8 in 10 CFOs at large companies are using AI or actively considering it, and AI pricing models continue to evolve as adoption scales.
The pricing pressure has a structural cause: building and running frontier AI models requires substantial computing infrastructure, and those costs compound as usage rises. Model makers are not yet profitable at scale, and usage-based pricing is one mechanism providers are using to close that gap as adoption grows.
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