Token Usage

If you are an administrator responsible for monitoring AI spend, this page is for you. Token Usage helps you track how much each LLM model is costing your organization by showing consumption broken down by model, day, and month, so you can identify heavy usage patterns, set budgets, and avoid unexpected bills.

The Token Usage page (/token-usage) gives administrators a clear view of how many tokens are being consumed by the LLM integrations: broken down by model, day, and month. This is the primary tool for tracking AI costs and identifying heavy usage patterns.

Period Selector

At the top of the page, a month navigator lets you move forward and backward through calendar months. All cards and tables update instantly when the period changes.

◀  January 2025  ▶

Summary Cards

Four cards provide an at-a-glance snapshot of the selected month:

Card	Description
Total Requests	Number of LLM API calls made during the period
Input Tokens	Total tokens sent to the model (prompts, context, system messages)
Output Tokens	Total tokens received from the model (generated responses)
Total Tokens	Sum of input + output tokens

Values are displayed in a human-readable format (for example 1.2M, 45K, or 8.3K) rather than raw numbers.

Summary by Model

A monthly aggregation table grouping consumption by LLM instance:

Column	Description
Instance	The LLM instance name as configured in Administration
Vendor	Provider (e.g., OpenAI, Anthropic, Google, Azure, Ollama)
Model	Specific model identifier (e.g., `gpt-4o`, `claude-3-5-sonnet`)
Requests	Total requests to this instance in the period
Input	Total input tokens for this instance
Output	Total output tokens for this instance
Total	Combined token count for this instance

Use this table to compare token consumption across different providers and models, and to identify which instances account for the largest share of usage.

Daily Breakdown

A day-by-day table showing consumption per model across the selected month:

Column	Description
Date	Calendar day
Instance	LLM instance name
Vendor	Provider
Model	Model identifier
Input Tokens	Tokens sent that day
Output Tokens	Tokens received that day
Total Tokens	Combined token count for that day
Requests	Number of requests on that day

Days with no LLM activity do not appear in the table. This breakdown helps spot daily spikes, for example, a batch re-indexing job or a high-traffic event.

How Token Recording Works

Viglet Turing records token usage automatically on every LLM call: no extra configuration is needed.

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sequenceDiagram
    participant User
    participant API as Chat API
    participant LLM as LLM Provider
    participant DB as llm_token_usage table

    User->>API: Chat request
    API->>LLM: Send prompt
    LLM-->>API: Response + token metadata
    API->>DB: recordUsage()<br/>(instance, vendor, model, username,<br/>input / output / total tokens, timestamp)
    API-->>User: Return response

What gets recorded on each call:

Field	Description
Instance	Which LLM instance handled the request
Vendor	Provider name
Model	Model identifier from the response metadata
Username	The authenticated user who triggered the call
Input Tokens	Extracted from the LLM response metadata
Output Tokens	Extracted from the LLM response metadata
Total Tokens	Input + output
Timestamp	Date and time of the request

The same llm_token_usage row also carries a frozen cost_usd plus the originating agent and cost stage (chat.live / chat.background / chat.skill), which is what powers Cost Governance. One table, two views.

API Endpoint

Token usage data is also available via the REST API. See REST API Reference → Token Usage API for endpoint details and examples.

Page	Description
Cost Governance	The USD-spend layer on top of these token counts
LLM Instances	The models whose consumption is tracked here
Observability	Token/cost metrics in Prometheus + Grafana