GitHub Copilot has become an important part of our development workflow in Visual Studio Code. What started as inline code completion has developed into a capable coding agent that can analyse repositories, edit multiple files and execute increasingly complex development tasks.
Like many digital services, AI tools were initially priced aggressively to encourage rapid adoption. Now that developers and organisations are becoming increasingly dependent on them, prices and usage limits are gradually increasing. Running these models remains expensive, so it is unlikely that AI usage will become cheaper or less restricted in the near future.
That makes it more important than ever to create awareness around token consumption and reduce unnecessary usage. Before we can prevent tokens from being wasted, we first need insight into how many credits we are consuming and which activities are responsible.
For developers using GitHub Copilot Business or Enterprise through their employer, reaching a limit will not usually result in a personal bill. It can, however, prevent you from continuing to use Copilot Chat, Agent Mode and other AI-powered features until additional credits become available or the allowance resets.
Fortunately, GitHub provides several ways to monitor your usage.
Check your Copilot usage in GitHub
Open your personal GitHub settings and navigate to: Profile picture → Settings → Copilot → Features. You can also open the page directly: github.com/settings/copilot/features. The Usage section shows how much of your included AI Credit allowance has already been consumed.
This is the official usage information connected to your account, so it should be your main reference when checking how close you are to your limit. For professional Copilot plans, inline suggestions and Next Edit Suggestions do not consume AI Credits. The relevant usage comes primarily from features such as Copilot Chat, Agent Mode, Copilot CLI and other model-driven interactions.
Check your usage from Visual Studio Code
You can also view your current usage without leaving Visual Studio Code. Click the Copilot icon in the status bar to see the limits applying to your account and when they reset.
This is useful after a long Agent Mode session or when using more capable reasoning models. A single task may involve several model calls, large amounts of repository context and multiple sub-agents behind the scenes.
The built-in indicator tells you how much usage remains, but it does not necessarily explain which sessions consumed those credits.
Understand where your credits are going
For more details, you can install the third-party Copilot Cost & Token Tracker extension for Visual Studio Code. The extension reads the Copilot agent logs stored locally by Visual Studio Code and creates a dashboard showing:
- Estimated AI Credits per session
- Usage broken down by model
- Input, output and cached tokens
- Main-agent and sub-agent calls
- Aggregated usage over time
This can help identify why one session consumed considerably more than another.
Perhaps Copilot repeatedly loaded a large amount of context. Maybe it used a powerful model for a relatively simple task, created several sub-agents or kept retrying an unsuccessful approach.
The extension should be treated as a diagnostic tool rather than an official billing overview. It only sees the sessions available in your local Visual Studio Code logs and calculates its results using its own pricing information.
A useful distinction is: GitHub shows how close you are to your actual limit. The extension helps explain which local sessions consumed your credits.
As with any third-party extension used in a professional environment, check whether your organisation permits its installation.
My thoughts
Usage-based pricing is understandable. A short coding question and an autonomous agent working across an entire repository clearly do not require the same amount of computing power. However, we are paying for the tokens consumed, not for the value of the result.
An agent can use a large amount of context, call several tools and generate thousands of tokens, only to produce a solution that does not compile or completely misunderstands the problem. Those tokens cost exactly the same as tokens that resulted in a perfect solution.
I would like to have the option to rate the value of an AI response. Was it useful, partly useful or completely useless? When a solution is terrible, should we really pay the full price for the tokens used to create it?
In practice, this would be difficult to implement fairly. The model provider has already incurred the infrastructure costs, the quality of an answer is subjective, and a refund system would be easy to abuse. LLM providers also cannot guarantee that every answer will be correct. I therefore do not expect a thumbs-down button to return our AI Credits anytime soon.
Still, it is an interesting thought. AI providers already ask us to rate their answers because that feedback helps improve their models. Perhaps future usage dashboards should not only show how many tokens we consumed, but also help us understand whether those tokens produced something valuable.