{
  "calculations": [],
  "fields": [
    "omni_dbt__quarterly_grr_and_nrr.firm_tier_adjusted",
    "omni_dbt__quarterly_grr_and_nrr.total_baseline_mrr"
  ],
  "filters": {
    "omni_dbt__quarterly_grr_and_nrr.baseline_date": {
      "kind": "BETWEEN",
      "left_side": "2025-05-01",
      "right_side": "2026-08-01",
      "type": "date"
    }
  },
  "limit": 100,
  "modelId": "e407bba6-4fae-4079-ac4e-cd6cf8fdf6f8",
  "pivots": [],
  "sorts": [
    {
      "column_name": "omni_dbt__quarterly_grr_and_nrr.firm_tier_adjusted",
      "sort_descending": true
    }
  ],
  "table": "omni_dbt__eds_all_accounts"
}

Cost is the LLM completion spend LiteLLM priced for each call, summed per request. Chandler is running on the Codex CLI, which draws ChatGPT plan quota rather than per-token API billing, so this figure is not money paid — it is what the same traffic would have cost on the API, priced from the token counts Codex reports. Read it as the size of the bill avoided. Two known limits: embedding spend is not recorded, so retrieval and matching cost is missing, and because the figure is one sum per request it cannot be split by model within a request — a request's classifier and agent calls can use different models while llm_model holds only one name. Only authenticated calls are logged, USAGE_LOG_ENABLED can switch logging off, and log writes are fail-soft — this is not a complete record of traffic. Questions are grouped into sessions: a session is exact when the caller echoed its id back to us and otherwise inferred from a 30-minute gap in that user’s activity, so a grouping is only as good as the source shown on the session itself. A call with no attributable user gets no session at all; those questions are listed separately rather than dropped.