AI Bridge is a smart proxy for AI. It acts as a man-in-the-middle between your users' coding agents / IDEs

and AI providers like OpenAI and Anthropic. By intercepting all the AI traffic between these clients and

All of these records are associated to an "interception" record, which maps 1:1 with requests received from clients but may involve several interactions with upstream providers. Interceptions are associated with a Coder identity, allowing you to map consumption and cost with teams or individuals in your organization:



These logs can be used to determine usage patterns, track costs, and evaluate tooling adoption.

This data is currently accessible through the API and CLI (experimental), which we advise administrators export to their observability platform of choice. We've configured a Grafana dashboard to display Claude Code usage internally which can be imported as a starting point for your tooling adoption metrics.



We provide an example Grafana dashboard that you can import as a starting point for your tooling adoption metrics. See[here](../examples/monitoring/dashboards/grafana/aibridge/README.md).

A sample Grafana dashboard for monitoring AI Bridge token usage, costs, and cache hit rates in Coder.


The dashboard includes three main sections with multiple visualization panels:

**Usage Leaderboards** - Track token consumption across your organization:

- Bar chart showing input, output, cache read, and cache write tokens per user

- Total usage statistics with breakdowns by token type

**Approximate Cost Table** - Estimate AI spending by joining token usage with live pricing data from LiteLLM:

- Per-provider and per-model cost breakdown

- Input, output, cache read, and cache write costs

- Total cost calculations with footer summaries

**Interceptions** - Monitor AI API calls over time:

- Time-series bar chart of interceptions by user

- Total interception count

**Prompts & Tool Calls Details** - Inspect actual AI interactions:

- User Prompts table showing all prompts sent to AI models with timestamps

- Tool Calls table displaying MCP tool invocations, inputs, and errors (color-coded for failures)

All panels support filtering by time range, username, provider (Anthropic, OpenAI, etc.), and model using regex patterns.

1.**Install the Infinity plugin**:`grafana-cli plugins install yesoreyeram-infinity-datasource`

2.**Configure data sources**:

-**PostgreSQL datasource** (`coder-observability-ro`): Connect to your Coder database with read access to`aibridge_interceptions`,`aibridge_token_usages`,`aibridge_user_prompts`,`aibridge_tool_usages` and`users`

-**Infinity datasource** (`litellm-pricing-data`): Point to`https://raw.githubusercontent.com/BerriAI/litellm/refs/heads/main/model_prices_and_context_window.json` for model pricing data

3.**Import**: Download[`dashboard.json`](https://raw.githubusercontent.com/coder/coder/main/examples/monitoring/dashboards/grafana/aibridge/dashboard.json) from this directory, then in Grafana navigate to**Dashboards** →**Import** →**Upload JSON file**. Map the data sources when prompted.

- Token usage leaderboards by user, provider, and model

- Filterable by time range, username, provider, and model (regex supported)