The AI Bill Is Coming Due: GitHub’s Copilot Move Has Startups On Alert

From June 1, the code repository platform GitHub moved its Copilot AI-coding assistant from a flat subscription-based model to a usage-based model. The usage here will be measured based on the number of AI tokens consumed, including the input, output, and cached tokens. Tokens are the fundamental units of text processed by AI models. The Microsoft-owned company announced the switch through a blog back on April 27; however, it came into effect earlier this week. 

GitHub launched the AI coding assistant in 2022 and it is widely regarded as one of the pioneers of AI-assisted software development. India is one of GitHub’s largest markets, with over 27 Mn developers on the platform, and the company says nearly 80% of new Indian developers use Copilot within their first week. Given GitHub’s dominance in software development workflows, the pricing change is likely to have a significant impact on India’s startup ecosystem.

Startups Gear Up For Bigger Bills

Under the new system, Copilot plans come with a monthly allocation of GitHub AI Credits, with users able to purchase additional credits if they exceed their limits. GitHub’s $10 Pro plan includes 1,500 AI credits, the $39 Pro+ plan offers 7,000 credits, and the $100 Max plan provides 20,000 credits. However, many users argue that estimating credit consumption is far from straightforward, particularly for complex coding tasks, large codebases, and extended AI-assisted workflows.

GitHub said in a statement to Inc42 that Copilot’s new pricing model is designed to reflect actual usage rather than a flat subscription fee. The company added that users would have access to spending limits, usage dashboards, and model-selection controls to better manage costs, while also introducing the Copilot Max plan for those requiring higher usage capacity.

“We use Copilot extensively across our internal development workflows. Earlier, the pricing was fixed, straightforward, and highly predictable. However, in just the past one or two weeks, I’ve seen a significant spike in usage. At the current rate, my consumption has increased by more than 300%, and I could end up paying several times what I previously spent in an entire month,” said Vikash Srivastava, cofounder and CTO of AI infrastructure startup Vobiz.

Srivastava added that the company’s monthly Copilot expenditure typically ranged between $500 and $700. However, under the new pricing structure and with growing usage, it exhausted its allocation within a week. The startup is now evaluating lower-cost alternatives, including Google’s Gemini and Alibaba-backed Qwen models.

Similarly, hostel chain operator Zostel and its hacker house initiative Zo House are reassessing their AI tooling strategy. “With the move to token-based pricing, we are actively pulling back on using Copilot for heavy planning or extended review loops, and leaning on other available tools in parallel for that kind of grunt work. We also approach project scoping differently now, breaking down products and features into abstraction-led modular chunks that can be built independently and connected through a central architecture. That reduces the overall dependency on any single Copilot instance and naturally keeps token usage lean,” said CEO Aviral Gupta.

Gupta added that Zostel’s overall spending on AI tools currently ranges between ₹3 Lakh and ₹5 Lakh per month, with costs controlled through role-based allocations and selective use of models based on specific requirements.

Industry experts believe GitHub’s move signals a broader shift in how AI products will be priced going forward. “This move was always coming. Flat subscriptions were quietly cross-subsidising heavy users with light ones, and agentic coding consumes far more compute than that model assumed. GitHub is just the first big name to stop pretending otherwise,” said Satyajith M, CTO of IT services firm Hexaware.

He added that Hexaware has already enabled GitHub’s budget controls and usage dashboards and is increasingly managing Copilot expenditure in the same way it manages cloud infrastructure spending, rather than treating it as a conventional software subscription cost.

Growing Bills Act As AI Party Pooper

For many companies, the rapid adoption of AI is now being followed by a new challenge: managing ballooning AI bills. As organisations integrate AI tools into software development, customer support, content creation, and internal operations, costs are becoming increasingly difficult to predict.

According to Goldman Sachs Research, the rise of agentic AI is expected to increase AI token consumption by 24 times by 2030. Although the cost per token is declining, overall spending on AI software and computing infrastructure is climbing sharply, raising concerns among enterprises about the long-term returns on their AI investments.

Earlier this year, ride-hailing giant Uber acknowledged that AI spending had grown sharply and said it was becoming more deliberate about where and how expensive models were deployed. As per a Bloomberg report, the company blew up its Claude Code budget in just four months, and now to manage costs, has set usage caps. Similarly, Microsoft reportedly canceled most of its internal Claude Code licenses, partly over cost, six months after rolling them out.  

This is a visible shift from the tokenmaxxing phenomenon that gained prominence in the last few months. Tokenmaxxing refers to the practice of maximising the consumption of AI tokens by employees, treating the usage as a direct metric of productivity. Companies like Meta and Google were reported to have included this metric in their performance review.

[Edited by Nikhil Subramaniam]

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