How does the Token Usage Dashboard Work?
Brim’s Token Usage Dashboard gives you clear, transparent insight into how your projects use compute over time. With this dashboard, you can monitor cost drivers, understand which variables and models consume the most tokens, and make informed decisions to optimize your workflows.
This guide walks through each section of the dashboard and how to use it to manage spend.
IMPORTANT: Data on the Token Usage Dashboard starts on the day your institution upgraded to the December 2025 version of Brim. Token usage from before that date won't be represented.
Why Token Usage Matters
Large language models (LLMs) process text in “tokens,” and token usage directly affects compute cost.
The Token Usage Dashboard helps you:
- Track usage over time across projects
- Understand which variables drive the most compute
- Compare models by both usage and cost
- Forecast expected spend
- Identify opportunities to optimize workflows (e.g., via conditional generation, variable redesign)
To better match token counts and costs from your compute provider, Brim excludes cached tokens from its counts. This means that if you run the exact same generation twice in a row in a short time period, the token count will increment by a lower than expected amount, or even zero.
For the most accurate token counts, we recommend checking with your compute provider.
Where to Find the Token Usage Dashboard
You can find the Token Usage Dashboard in two places:
- Admins only:
- Settings > Admin
- Projects
- "View Usage Dashboard" button
- You can filter by any combination of projects or all projects.
- Project Owners:
- When the Project you are an Owner for is open, go to Settings > Token Usage
- You will see the current project selected by default, but can select all projects for which you are the Owner.
Filters & Settings
Use the panel on the left to customize the view.
Projects
Select a single project or view All Projects for which you have permissions combined.
Date Range
Use From and To to filter usage for a specific time period—such as a monthly billing cycle or a particular phase of your study. Keep in mind that the most recent month is likely partial.
Average Cost per Million Tokens
Enter your cost-per-million-tokens based on your LLM provider’s pricing.
Brim uses this value to estimate spend throughout the dashboard.
Click "Apply" to apply the filters to the data on the right.
You can reset filters at any time using Reset.
High-Level Metrics
At the top of the dashboard, Brim summarizes key compute indicators:
Total Tokens
Your overall token usage for the selected projects and timeframe, with an estimated cost based on your entered cost-per-million tokens.
Average per Document
This is a rough estimate of how many tokens are processed for each document, with an estimated cost based on your entered cost-per-million tokens.
- NOTE: This is a very basic calculation of Total Tokens in the timeframe / Total Documents uploaded to the project. This can make the average skewed if you haven't generated for every document.
Average per Patient
This is a rough estimate of how many tokens are processed for each patient, with an estimated cost based on your entered cost-per-million tokens.
- NOTE: This is a very basic calculation of Total Tokens in the timeframe / Total Patients uploaded to the project. This can make the average skewed if you haven't generated for every patient.
Token Usage Over Time
This time-series chart shows how your token usage trends month by month.
You can use this view to identify:
- Onboarding spikes
- Months where new variables or models were added
- Effects of optimization or conditional logic
- Unexpected anomalies in compute consumption
Hover over any point to see exact usage values.
Tokens by Project
This graph is only visible if you have multiple projects selected in Filters.
See which projects consume the most compute, ranked from highest to lowest.
This view is especially valuable for:
- Understanding which projects resource-intensive
- Spotting projects where optimization could yield large cost savings
- Reporting usage to stakeholders by project or study team
Each bar includes both token usage and estimated cost (using your cost-per-million token value).
Tokens by Variable
This breakdown shows which variables drive the most token usage.
Use this view to:
- Identify high-cost variables
- Detect variables that might benefit from conditional generation
- Compare performance across variable types
- Prioritize optimization work
Tokens by Model
This graph is only visible if you have token usage for more than 1 model.
A pie chart summarizes the distribution of token usage across model types (e.g., gpt-4o-mini, gpt-5o-mini, gpt-4o).
This helps answer questions like:
- Are premium models driving most of your spend?
- How much compute is attributable to lightweight vs. heavyweight models?
- Would switching models materially change cost?
Estimated Cost by Model
This graph is only visible if you have token usage for more than 1 model.
This section uses your entered pricing for each model to estimate total spend over the selected timeframe.
You’ll see:
- A per-model breakdown of estimated cost
- A combined Total Estimated Cost
- A visual pie chart showing which models drive the majority of spend
These are approximate calculations; they omit details like cached results and input vs. output token ratios. However, they provide directional insight for budgeting and optimization.
