Give me the total monthly Data Health add-on revenue (combining MRR for Direct/Corporate accounts and ACV for Partner/Accountant accounts as per our standard rule) for every month from January 2024 to June 2026 — all 30 months, please don't truncate the table.
Per-step detail — each LLM call, each Omni request, each rejected draft —
is traced to stdout only and is not stored, so it cannot be shown here.
Query attempts counts run_query executions, not model turns.
{
"calculations": [],
"fields": [
"omni_dbt__eds_calendar_day.date_day[month]",
"omni_dbt__eds_bookings.total_data_health_contract_monthly_acv"
],
"filters": {
"omni_dbt__eds_all_accounts.finance_account_type": {
"kind": "EQUALS",
"type": "string",
"values": [
"Accountant"
]
},
"omni_dbt__eds_all_accounts.is_dext_demo": {
"is_negative": true,
"treat_nulls_as_false": true,
"type": "boolean"
},
"omni_dbt__eds_all_accounts.salesforce_account_type": {
"is_negative": true,
"kind": "EQUALS",
"type": "string",
"values": [
"Reseller"
]
},
"omni_dbt__eds_bookings.has_paid_subscription": {
"is_negative": false,
"type": "boolean"
},
"omni_dbt__eds_calendar_day.date_day[date]": {
"kind": "BETWEEN",
"left_side": "2026-02-01",
"right_side": "2026-07-01",
"type": "date"
},
"omni_dbt__eds_calendar_day.is_last_day_of_month": {
"is_negative": false,
"type": "boolean"
}
},
"limit": 1000,
"modelId": "e407bba6-4fae-4079-ac4e-cd6cf8fdf6f8",
"pivots": [],
"sorts": [
{
"column_name": "omni_dbt__eds_calendar_day.date_day[month]",
"sort_descending": false
}
],
"table": "omni_dbt__eds_all_accounts"
}
No feedback linked to this request.
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.