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Microsoft plans to switch GitHub Copilot subscribers to a more precise token-based billing system this June, reflecting the company's efforts to manage escalating AI computation costs and improve pricing fairness.
Microsoft is set to roll out a significant change in its billing model for GitHub Copilot, transitioning all subscribers to token-based billing starting in June. According to internal documents reviewed by Where’s Your Ed At, this move aims to address the rising costs associated with AI compute and better align usage with actual resource consumption.
The shift to token-based billing represents a fundamental change in how GitHub Copilot is monetized. Currently, users are charged based on a fixed number of requests, which can vary widely in terms of computational demand. The new model will charge users for the actual tokens processed, providing a more granular and fairer approach to billing. This transition is expected to impact both individual and enterprise subscribers, with varying tiers of service and pricing.
In addition to the billing changes, Microsoft has already taken steps to manage usage and costs:

Under the current model, GitHub Copilot users have a set number of "requests" per month. For example:
In the new token-based system, users will pay for the actual tokens processed. Tokens are units of text input and output, with more complex models consuming more tokens. For instance, Claude Opus 4.7 costs $5 per million input tokens and $25 per million output tokens.
For organizations, the pooled AI credits offer a flexible solution to manage resource allocation across teams. This can lead to cost savings for companies that have varying usage patterns among their users. For Microsoft, this transition is a strategic move to ensure sustainable growth of GitHub Copilot by aligning costs more closely with usage.
Microsoft’s transition to token-based billing for GitHub Copilot is a strategic move to address rising computational costs and ensure the long-term sustainability of the service. While it introduces new challenges, particularly in terms of customer adoption and cost predictability, it also offers opportunities for more efficient resource management and potentially lower costs for organizations with flexible usage patterns.
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Marcus began tracking AI's market implications in 2016, noticing AI-related patent filings accelerating ahead of earnings upgrades before most of the sell-side had caught on. A former fixed-income quantitative analyst, he spent two decades building models that priced risk across emerging markets before pivoting to cover the economic impact of AI full-time. His writing translates opaque technical developments into clear risk/reward terms — and he's rarely diplomatic about the gap between AI valuations and underlying fundamentals. He believes most market participants still underestimate AI's long-run deflationary effect on knowledge work.
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