AI Cost & Credits
AI Cost & Credits helps you monitor GitHub Copilot AI Credit consumption, usage value, budget risk, and user-level cost patterns inside Oobeya AI Impact.
What you can track
Use this page to answer:
How many AI Credits were consumed?
What is the gross usage value?
How much usage is covered by included or discounted credits?
Is there any net billable cost?
How much of the AI Credit pool has been used?
Which models consume the most credits?
Which users have the highest usage?
Are there budget risks or unusual usage patterns?
Accessing the page
Go to:
AI Impact → Cost & Credits
The page follows the same global filters as AI Impact.
Team
Filter results by all teams or a selected team
Type
Filter by available AI Impact type
Working Days
Include or exclude non-working days from calculations
Daily / Weekly / Monthly
Change the aggregation period
Date Range
Select the reporting period
Summary cards
The top section gives a quick overview of AI Credit usage and cost status.
Total Users
Number of users included in the selected scope
AI Credit Pool Value
Dollar value of the available AI Credit pool
Credit Pool Usage
Percentage of the available credit pool already consumed
Gross Usage Value
Total consumed AI Credit value before discounts or included usage
Discounted / Included Usage
Usage value covered by included credits or discounts
Net Billable Cost
Cost expected to be billed after discounts and included usage
Total AI Credits Used
Total AI Credits consumed in the selected period
Remaining Credits
Credits remaining in the available pool
Forecasted Month-End Usage
Estimated month-end AI Credit usage
Forecasted Overage
Estimated credits above the available pool
Avg Daily Burn Rate
Average daily AI Credit consumption
Cost per Active User
Gross usage value divided by active users
Sections
1. Summary
The Summary tab shows usage and cost trends over time.
It includes:
Gross, included/discounted, and net billable usage trend
Daily credit burn chart
Latest collected report list
Use this tab to understand whether AI Credit consumption is increasing, decreasing, or spiking during the selected period.
2. Pool & Forecast
The Pool & Forecast tab shows how much of the available AI Credit pool has been consumed and whether the current usage trend may create overage risk.
It includes:
Total Credit Pool
Available AI Credit quota
Used Credits
Credits already consumed
Remaining Credits
Credits still available
Expected Usage by Today
Expected usage based on elapsed time in the period
Over / Under Expected
Difference between actual and expected usage
Projected Pool Exhaustion
Estimated date when the pool may be depleted
3-Day Burn Rate
Average usage based on the last 3 days
7-Day Burn Rate
Average usage based on the last 7 days
Forecasted Month-End Usage
Projected total usage by the end of the month
Use this tab to identify whether the organization is on track, under-consuming, or likely to exceed the available pool.
3. Models
The Models tab breaks down AI Credit consumption by model.
It includes:
Credit share by model
Cost by model table
Gross, discount, and net values per model
User count per model
Auto-selected model labels where available
Use this tab to identify which models are responsible for most AI Credit consumption.
Typical questions:
Which model consumes the most credits?
Are premium models driving most of the cost?
How much usage comes from auto-selected models?
Which models should be reviewed for optimization?
4. Users
The Users tab shows user-level AI Credit consumption.
It includes:
User
GitHub / mapped Oobeya user
Credits Used
Total AI Credits consumed by the user
Gross Value
Consumed credit value
Discounted Value
Value covered by included/discounted usage
Net Billable Cost
Billable cost after discounts
Pool Impact
User share of total credit usage
Primary Model
Most-used model by the user
Models Used
Number of different models used
Daily Burn Rate
Average daily usage
Projected Month-End Credit
Estimated month-end usage
Status
Usage status such as Risk or Critical
Click a user to open the detail drawer.
The user detail drawer shows:
Credits used
Gross value
Net billable cost
Daily burn rate
Projected month-end credits
Status
AI Credit usage timeline
Model breakdown
User-level data should be interpreted as usage visibility, not individual performance evaluation.
5. Budget Recommendations
The Budget Recommendations tab provides soft budget recommendations based on current and forecasted usage.
It includes:
User
User receiving the recommendation
Current Budget
Current assigned AI Credit budget
Credits Used
Credits already consumed
Remaining Budget
Remaining budget amount
Forecasted Month-End Usage
Estimated end-of-month usage
Projected Increase
Expected increase above current budget
Recommended New Budget
Suggested adjusted budget
Priority
Recommendation priority
Reason
Explanation of the recommendation
Status
Current recommendation status
Budget recommendations are advisory. They help teams review usage patterns and capacity needs.
6. Risks & Insights
The Risks & Insights tab highlights important usage and budget signals.
Examples include:
Credit pool exhaustion risk
Usage fully covered by included credits
Users with critical usage patterns
High-cost model concentration
Forecasted overage risk
Risk levels may include:
Info, Low, Medium, High, Critical
Use this section to quickly identify items that may require review.
7. Raw Report
The Raw Report tab displays the collected GitHub AI usage report rows.
Use this tab when you need to inspect the source-level report data behind the dashboards.
Key definitions
AI Credits
Usage unit used to measure GitHub Copilot AI consumption
Gross Usage Value
Dollar value of consumed AI Credits before discounts
Discounted / Included Usage
Usage value covered by included credits or discounts
Net Billable Cost
Remaining cost after included usage and discounts
Credit Pool
Available AI Credit quota for the selected period
Burn Rate
Average AI Credit consumption over time
Forecasted Overage
Estimated usage above the available credit pool
Recommended workflow
Start with Summary to understand the overall trend.
Open Pool & Forecast to check pool usage and overage risk.
Review Models to identify high-consumption models.
Review Users to understand user-level usage patterns.
Check Budget Recommendations for projected capacity needs.
Use Risks & Insights to focus on items needing attention.
Use Raw Report only when you need source-level details.
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